📝 Transferability of Representations Learned Using Supervised Contrastive Learning Trained on a Multi-Domain Dataset 🔭

"Supervised Contrastive Learning models are trained to maximize agreement between differently augmented views of the same image while minimizing agreement between augmented views of different images from the same batch of images." [gal30b+] 🤖 #CV

⚙ https://github.com/rois-codh/kaokore
🔗 https://arxiv.org/abs/2309.15486v1 #arxiv

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📝 Subjective Face Transform Using Human First Impressions 🔭

"A face image is encoded into a latent code that is used to reconstruct the input image as well as to predict its attribute scores (such as perceived trustworthiness and attractiveness)." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.15381v1 #arxiv

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📝 Generating Visual Scenes From Touch 🔭

"Learns a latent diffusion model that converts a touch signal into a sequence of images, which are then decoded to produce the final synthesis result (or vice versa)." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.15117v1 #arxiv

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📝 InternLM-XComposer: A Vision-Language Large Model for Advanced Text-Image Comprehension and Composition 🔭

"InternLM-XComposer can effortlessly generate coherent and contextual articles that seamlessly integrate images, providing a more engaging and immersive reading experience with rich knowledge and comprehension." [gal30b+] 🤖 #CV

⚙ https://github.com/InternLM/InternLM-XComposer
🔗 https://arxiv.org/abs/2309.15112v1 #arxiv

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📝 LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models 🔭

"Proposes LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model." [gal30b+] 🤖 #CV

⚙ https://github.com/Breakthrough/PySceneDetect
🔗 https://arxiv.org/abs/2309.15103v1 #arxiv

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📝 The Surveillance AI Pipeline 🔭

"A complex pipeline with many steps and actors involved, including universities, start-ups, and multinational corporations, as well as governments and international institutions." [gal30b+] 🤖 #CV #CY

🔗 https://arxiv.org/abs/2309.15084v1 #arxiv

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📝 An Ensemble Model for Distorted Images in Real Scenarios 🔭

"A combination of data enhancement, detection box ensemble, denoiser ensemble, super-resolution models, and transfer learning is used to make the model achieve excellent performance on the CDCOCO dataset." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14998v1 #arxiv

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📝 IAIFNet: An Illumination-Aware Infrared and Visible Image Fusion Network 🔭

"The proposed IAIFNet consists of an illumination enhancement network and an image fusion network, with the adaptive differential fusion module (ADFM) and salient target aware module (STAM) for the effective integration of features and targets." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14997v1 #arxiv

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📝 Robust Sequential DeepFake Detection 🔭

"SeqFakeFormer and SeqFakeFormer++ are proposed for Seq-DeepFake detection based on a newly constructed large-scale Seq-DeepFake dataset." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14991v1 #arxiv

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📝 MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection 🔭

"Mixture of Calibrated Experts (MoCaE) consists of a set of expert models trained with different configurations and/or data augmentation strategies that are then calibrated." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14976v1 #arxiv

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📝 FEC: Three Finetuning-Free Methods to Enhance Consistency for Real Image Editing 🔭

"Proposes a new image sampling method called FEC, which consists of three sampling methods, each designed for different editing types and settings, and achieves two important goals in image editing task: ensuring successful reconstruction and improving the performance of many editing methods." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14934v1 #arxiv

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📝 Noise-Tolerant Unsupervised Adapter for Vision-Language Models 🔭🧠

"NtUA works as a key-value cache that formulates visual features and predicted pseudo-labels of the few-shot unlabelled target samples as key-value pairs." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.14928v1 #arxiv

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📝 Face Cartoonisation for Various Poses Using StyleGAN 🔭

"Trains an encoder to capture both pose and identity information from images and generate a corresponding embedding within the StyleGAN latent space for cartoonisation purpose by passing the embedding through a pre-trained generator." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14908v1 #arxiv

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📝 Pre-Training-Free Image Manipulation Localization Through Non-Mutually Exclusive Contrastive Learning 🔭

"Proposes a pivot structure to constantly change the role of contour patches between positives and negatives while training and thus avoids spatial corruption caused by the role-changing process with a pivot-consistent loss." [gal30b+] 🤖 #CV

⚙ https://github.com/Knightzjz/NCL-IML
🔗 https://arxiv.org/abs/2309.14900v1 #arxiv

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📝 Nearest Neighbor Guidance for Out-of-Distribution Detection 🔭

"Proposes a method called Nearest Neighbor Guidance (NNGuide) to guide the classifier-based OOD score to respect the boundary geometry of the data manifold." [gal30b+] 🤖 #CV

⚙ https://github.com/roomo7time/nnguide
🔗 https://arxiv.org/abs/2309.14888v1 #arxiv

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📝 Cross-Dataset-Robust Method for Blind Real-World Image Quality Assessment 🔭

"First, many individual BIQA models based on SwinT are trained on different real-world BIQA datasets respectively and jointly used to generate pseudo-labels which adopts the probability of relative quality of two random images instead of fixed quality score." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14868v1 #arxiv

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📝 ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios 🔭

"A dataset and benchmarks on four tasks related to human-object interactions in the industrial domain: 1) untrimmed action detection, 2) egocentric human-object interaction detection, 3) short-term object interaction anticipation and 4) natural language understanding of intents and entities." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14809v1 #arxiv

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📝 Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation 🔭

"Designs a novel motion-as-option network that can take RGB images and optical flow maps as an input at the same time, and produce two different predictions." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14786v1 #arxiv

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📝 Multi-Label Feature Selection Using Adaptive and Transformed Relevance 🔭

"An information-theoretical filter-based approach, which is scalable to extensive feature and label spaces, addressing multi-label classification scenarios where instances are associated with multiple class labels simultaneously." [gal30b+] 🤖 #CV

⚙ https://github.com/Sadegh28/ATR
🔗 https://arxiv.org/abs/2309.14768v1 #arxiv

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📝 InvKA: Gait Recognition via Invertible Koopman Autoencoder 🔭

"The gait feature in the embedding space, which is based on Koopman operator theory, can capture complex kinematic features of gait cycles, namely the Koopman operator." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14764v1 #arxiv

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📝 Image Denoising via Style Disentanglement 🔭

"The style of the noisy image is encoded with a style module and then transferred to the clean image, which induces low-response activations for noise features and high-response activations for content features in the feature space." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14755v1 #arxiv

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📝 Advanced Volleyball Stats for All Levels: Automatic Setting Tactic Detection and Classification with a Single Camera 🔭

"Uses a novel set trajectory classifier to identify setters’ movements and the ball’s trajectory from a single camera view, and to detect the opposing team's right-side hitter’s current row (front or back) during gameplay." [gal30b+] 🤖 #CV

⚙ https://github.com/volleyIEEE/VolleyStats
🔗 https://arxiv.org/abs/2309.14753v1 #arxiv

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📝 SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion 🔭

"Can generate high-quality fusion images from pairs of infrared and visible images, which can boost the performance of downstream computer-vision tasks, such as object detection and recognition." [gal30b+] 🤖 #CV

⚙ https://github.com/QiaoYang-CV/SSPFUSION
🔗 https://arxiv.org/abs/2309.14745v1 #arxiv

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📝 Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement 🔭

"BDCE is a bootstrap diffusion model that exploits the learning of distribution of the curve parameters instead of the normal-light image itself, and denoise module is applied in each iteration of curve adjustment to denoise the intermediate enhanced result of each iteration." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14709v1 #arxiv

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📝 Text-to-Image Generation for Abstract Concepts 🔭

"A framework of Text-to-Image generation for Abstract Concepts (TIAC), which is able to visualize abstract concepts in a more comprehensive manner, is designed." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14623v1 #arxiv

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📝 Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator 🔭

"Free-Bloom harnesses large language models as the director to generate a semantic coherence prompt sequence, while pre-train latent diffusion models as the animator to generate the high fidelity frames." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.14494v1 #arxiv

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📝 HPCR: Holistic Proxy-Based Contrastive Replay for Online Continual Learning 🧠🔭

"The proxy-based contrastive replay (PCR) alleviates the catastrophic forgetting issue via contrastive learning with anchor-to-proxy pairs, while the holistic proxy-based contrastive replay (HPCR) learns more finegrained semantic information with anchor-to-sample pairs." [gal30b+] 🤖 #LG #CV

🔗 https://arxiv.org/abs/2309.15038v1 #arxiv

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📝 Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization 🧠🔭

"The proposed TRIBE framework is built upon a tri-net architecture with balanced batchnorm to adapt the source pre-trained model towards a class imbalanced testing data stream with continual domain shift." [gal30b+] 🤖 #LG #CV

⚙ https://github.com/Gorilla-Lab-SCUT/TRIBE
🔗 https://arxiv.org/abs/2309.14949v1 #arxiv

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📝 Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation 🧠🔭

"Domain-guided Conditional Diffusion Model generates high-fidelity and diversity samples for the target domain, and generated samples help existing UDA methods transfer from the source domain to the target domain more easily, thus improving the transfer performance." [gal30b+] 🤖 #LG #CV

🔗 https://arxiv.org/abs/2309.14360v1 #arxiv

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📝 GLOBER: Coherent Non-Autoregressive Video Generation via GLOBal Guided Video DecodER 🔭

"Proposes a novel non-autoregressive method GLOBER, which first generates global features to obtain comprehensive global guidance and then synthesizes video frames based on the global features to generate coherent videos." [gal30b+] 🤖 #CV

⚙ https://github.com/iva-mzsun/GLOBER
🔗 https://arxiv.org/abs/2309.13274v1 #arxiv

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📝 Order-Preserving Consistency Regularization for Domain Adaptation and Generalization 🔭🧠

"OCR constrains the prediction of two augmented views to be the order-preserving in probability, making the model robust to task-irrelevant transformations such as domain-specific attributes." [gal30b+] 🤖 #CV #LG

⚙ https://github.com/advboxes/AdvBox
🔗 https://arxiv.org/abs/2309.13258v1 #arxiv

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📝 RTrack: Accelerating Convergence for Visual Object Tracking via Pseudo-Boxes Exploration 🔭

"The RTrack tracker is based on a new object representation method that uses a set of sample points to generate a pseudo-bounding box to capture the spatial extent information and emphasize local areas." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.13257v1 #arxiv

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📝 Multi-Modal Domain Adaptation for REG via Relation Transfer 🔭

"Uses a specially relation-tailored approach to enhance inter-domain relations and transfer relations between domains to tackle multi-modal domain transfer problem in the REG task." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.13247v1 #arxiv

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📝 RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias 🔭

"We enhance the adversarial robustness by increasing the proportion of high-frequency structural robust biases, thus mitigating the susceptibility to robustness issues, and introduce a novel Robust Bias Transformer-based Structure (RBFormer) that shows robust superiority compared to several existing baseline structures." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.13245v1 #arxiv

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📝 Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation 🔭

"Proposes a spatial-temporal knowledge-embedded transformer (STKET) that incorporates prior spatial-temporal knowledge into the multi-head cross-attention mechanism to learn more representative relationship representations for video scene graph generation (VidSGG)." [gal30b+] 🤖 #CV

⚙ https://github.com/HCPLab-SYSU/STKET
🔗 https://arxiv.org/abs/2309.13237v1 #arxiv

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📝 ClusterFormer: Clustering as a Universal Visual Learner 🔭

"Introduces a new vision model, named ClusteRFormer, based on the CLUSTERing paradigm with TransFORMer, a new Transformer architecture that introduces recurrent cross-attention, a mechanism for updating cluster centers that facilitates strong representation learning." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.13196v1 #arxiv

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📝 Trading-Off Mutual Information on Feature Aggregation for Face Recognition 🔭🧠

"Aggregates the face embeddings from two state-of-the-art (SOTA) face recognition models, ArcFace and AdaFace, using the transformer self-attention mechanism." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.13137v1 #arxiv

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📝 Zero-Shot Object Counting with Language-Vision Models 🔭

"Uses large language-vision models to generate class prototypes from the input class name and use them to select the patches containing the target objects for counting exemplars, as well as a ranking model to estimate the counting error of each patch." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.13097v1 #arxiv

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📝 C$^2$VAE: Gaussian Copula-Based VAE Differing Disentangled From Coupled Representations with Contrastive Posterior 🧠🔭

"Presents a self-supervised variational autoencoder to jointly learn disentangled and dependent hidden factors and then enhance disentangled representation learning by a self-supervised classifier to eliminate coupled representations." [gal30b+] 🤖 #LG #CV

🔗 https://arxiv.org/abs/2309.13303v1 #arxiv

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📝 Spatial-Frequency Channels, Shape Bias, and Adversarial Robustness 🧠🔭

"Critical band masking is an established tool that can reveal the frequency-selective filters used for object recognition in humans and neural networks by measuring the sensitivity of categorization performance to noise added at each spatial frequency." [gal30b+] 🤖 #LG #CV

🔗 https://arxiv.org/abs/2309.13190v1 #arxiv

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📝 Flow Factorized Representation Learning 🧠🔭

"A novel setup bringing new understandings to both disentanglement and equivariance, achieving higher likelihoods on standard representation learning benchmarks while also demonstrating a degree of robustness and generalizability approaching the ultimate goal of usefully factorized representation learning." [gal30b+] 🤖 #LG #CV

⚙ https://github.com/KingJamesSong/latent-flow
🔗 https://arxiv.org/abs/2309.13167v1 #arxiv

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📝 Detect Every Thing with Few Examples 🔭

"DE-ViT utilizes vision-only backbones and improves general detection ability by transforming multi-classification tasks into binary classification tasks while bypassing per-class inference, and a novel region propagation technique for localization." [gal30b+] 🤖 #CV

⚙ https://github.com/mlzxy/devit
🔗 https://arxiv.org/abs/2309.12969v1 #arxiv

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📝 Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation 🔭

"BAS learns the whole region of the object by using foreground region guidance and area constraint, which can achieve a more effective localization performance by using activation value to learn more object regions." [gal30b+] 🤖 #CV

⚙ https://github.com/wpy1999/BAS-Extension
🔗 https://arxiv.org/abs/2309.12943v1 #arxiv

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📝 Domain Adaptive Few-Shot Open-Set Learning 🔭

"A novel approach called Domain Adaptive Few-Shot Open Set Recognition (DA-FSOS) and an architecture named DAFOSNET, which learns a shared and discriminative embedding space while creating a pseudo open-space decision boundary." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.12814v1 #arxiv

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📝 LMC: Large Model Collaboration with Cross-Assessment for Training-Free Open-Set Object Recognition 🔭

"Learns from multiple large models pre-trained through different paradigms to tackle open-set recognition in a training-free manner, with novel designs to effectively extract knowledge from large models." [gal30b+] 🤖 #CV

⚙ https://github.com/Harryqu123/LMC}{here
🔗 https://arxiv.org/abs/2309.12780v1 #arxiv

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📝 Transformer-Based Image Compression with Variable Image Quality Objectives 🔭

"Uses a single Transformer-based auto-encoder to compress an image with a variable quality objective by adaptively generating prompt tokens for the encoder and/or decoder using a prompt generation network." [gal30b+] 🤖 #CV #MM

🔗 https://arxiv.org/abs/2309.12717v1 #arxiv

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📝 Mixed Attention Auto Encoder for Multi-Class Industrial Anomaly Detection 🔭

"Uses spatial attention mechanism to capture global category information and channel attention mechanism to model the feature distributions of multiple classes, and it employs an adaptive noise generator and a multi-scale fusion module for the pre-trained features." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.12700v1 #arxiv

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📝 Exploiting Modality-Specific Features for Multi-Modal Manipulation Detection and Grounding 🔭

"Designs a novel framework that explores modality-specific features while preserving the capability for multi-modal alignment by introducing visual/language pre-trained encoders and dual-branch cross-attention." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.12657v1 #arxiv

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📝 RHINO: Regularizing the Hash-Based Implicit Neural Representation 🔭

"RHINO connects the input coordinate and the network additionally without modifying the architecture of current hash-based INRs by using an additional continuous analytical function, which ensures a seamless backpropagation of gradients from the network's output back to the input coordinates, thereby enhancing regularization." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.12642v1 #arxiv

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📝 Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects 🔭

"GCANet introduces a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention module." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.12641v1 #arxiv

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📝 Decision Fusion Network with Perception Fine-Tuning for Defect Classification 🔭

"The decision fusion network (DFNet) is designed to strengthen the decision ability of the network through a decision fusion module (DFM) and a perception fine-tuning module (PFM)." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.12630v1 #arxiv

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📝 BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection 🔭

"A novel BGFG-YOLO architecture by incorporating Bi-level Routing Attention (BRA), Generalized feature pyramid networks (GFPN), Forth detecting head, and Generalized-IoU (GIoU) bounding box regression loss into YOLOv8." [gal30b+] 🤖 #CV

⚙ https://github.com/mkang315/BGFG-YOLO
🔗 https://arxiv.org/abs/2309.12585v1 #arxiv

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📝 Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation 🔭

"TriKD utilizes the knowledge distillation skill to learn the complementary semantics among these encoders with two different architectures for semi-supervised semantic segmentation by using a few labeled images and a large amount of unlabeled images." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.12557v1 #arxiv

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📝 TextCLIP: Text-Guided Face Image Generation and Manipulation Without Adversarial Training 🔭

"TextCLIP is a unified framework for text-guided image generation and manipulation without complex multi-stage generation and adversarial training, which can also be extended to image-guided generation and manipulation." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.11923v1 #arxiv

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📝 AV-MaskEnhancer: Enhancing Video Representations Through Audio-Visual Masked Autoencoder 🔭

"AV-MaskEnhancer is an approach that combines the visual and audio information to learn more effective high-quality video representation by mask autoencoders (MAE)." [gal30b+] 🤖 #CV #MM

🔗 https://arxiv.org/abs/2309.08738v1 #arxiv

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📝 OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects 🔭

"Introduces Open-Illumination, an open dataset for material, lighting, and geometry reconstruction from single-view images and provide a comprehensive evaluation of several state-of-the-art inverse rendering methods on it." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07921v1 #arxiv

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📝 Unified Human-Scene Interaction via Prompted Chain-of-Contacts 🔭

"UniHSI translates language commands into task plans in the form of Chain of Contacts (CoC), and a unified controller turns CoC into the control sequence." [gal30b+] 🤖 #CV

⚙ https://github.com/OpenRobotLab/UniHSI
🔗 https://arxiv.org/abs/2309.07918v1 #arxiv

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📝 ALWOD: Active Learning for Weakly-Supervised Object Detection 🔭

"ALWOD is an active learning framework that utilizes a small labeled seed, a large weakly tagged set, and strategically selects images for human annotation using a new auxiliary image generator and a new acquisition function." [gal30b+] 🤖 #CV

⚙ https://github.com/seqam-lab/ALWOD
🔗 https://arxiv.org/abs/2309.07914v1 #arxiv

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📝 Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer Learning 🔭

"DiST uses a dual-encoder structure, where a pre-trained foundation model acts as the spatial encoder, and a lightweight network is introduced as the temporal encoder." [gal30b+] 🤖 #CV

⚙ https://github.com/alibaba-mmai-research/DiST
🔗 https://arxiv.org/abs/2309.07911v1 #arxiv

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📝 Generative Image Dynamics 🔭

"Trained with a frequency-coordinated diffusion process on a collection of natural motion trajectories extracted from videos of moving objects in the real world such as trees, flowers, candles, etc." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07906v1 #arxiv

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📝 A Novel Local-Global Feature Fusion Framework for Body-Weight Exercise Recognition with Pressure Mapping Sensors 🔭🧠

"Uses image processing techniques and the YOLO object detection to localize pressure profiles from different body parts and consider physical constraints, which is a one-step further from the existing studies using deep neural networks mainly focusing on global feature extraction." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.07888v1 #arxiv

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📝 Gradient Constrained Sharpness-Aware Prompt Learning for Vision-Language Models 🔭

"The trade-off performance correlates to both loss value and loss sharpness during optimization, while each of them are indispensable, but existing methods cannot always maintain high consistence with them." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07866v1 #arxiv

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📝 Decomposition of Linear Tensor Transformations 🔭

"A generalization of the Singular Value Decomposition (SVD) for matrices to tensors in higher dimension and it is based on the properties of the tensor space and of the Frobenius norm on tensor." [gal30b+] 🤖 #CV #NA

🔗 https://arxiv.org/abs/2309.07819v1 #arxiv

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📝 For a More Comprehensive Evaluation of 6DoF Object Pose Tracking 🔭

"The proposed benchmark uses a multi-view multi-object global pose refinement method to achieve sub-pixel and sub-millimeter alignment error for the YCBV dataset, and then introduces some improved evaluation methods to address the limitations of previous studies." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07796v1 #arxiv

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📝 Dataset Condensation via Generative Model 🔭

"A small generative model is trained to represent the distribution of the large dataset, and the small number of learnable parameters make it possible to scale up to ImageNet-1k." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07698v1 #arxiv

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📝 Towards Robust and Unconstrained Full Range of Rotation Head Pose Estimation 🔭

"Proposes an end-to-end approach for full head pose estimation from monocular 360${}^{\circ}$ images with a continuous representation." [gal30b+] 🤖 #CV

⚙ https://github.com/thohemp/6DRepNet360
🔗 https://arxiv.org/abs/2309.07654v1 #arxiv

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📝 Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement 🔭

"By introducing a hybrid neural representation that separates low-frequency and high-frequency scene regions, a simple yet effective image sharpening and denoising technique, and a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07640v1 #arxiv

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📝 SwitchGPT: Adapting Large Language Models for Non-Text Outputs 🔭

"Adapts large-scale language models (LLM) with a minimal multi-modal dataset to instruct LLMs to recognize the target output modality as directed by the instructions." [gal30b+] 🤖 #CV

⚙ https://github.com/xinke-wang/SwitchGPT
🔗 https://arxiv.org/abs/2309.07623v1 #arxiv

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📝 Road Disease Detection Based on Latent Domain Background Feature Separation and Suppression 🔭

"Proposes a new LDBFSS(Latent Domain Background Feature Separation and Suppression) network which could perform background information separation and suppression without domain supervision and contrastive enhancement of object features." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07616v1 #arxiv

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📝 Universality of Underlying Mechanism for Successful Deep Learning 🔭

"Each filter identifies small clusters of possible output labels, with additional noise selected as labels out of the clusters, and this feature is progressively sharpened with the layers, resulting in an enhanced signal-to-noise ratio and higher accuracy." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07537v1 #arxiv

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📝 A Multi-Scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing 🔭

"By designing a new learnable blur kernel proximal mapping module and deep proximal mapping modules for image domain, and combining the alternating iterations of shrinkage thresholds, MGSTNet focuses on learning deep geometric prior features to enhance image restoration." [gal30b+] 🤖 #CV #IT

🔗 https://arxiv.org/abs/2309.07524v1 #arxiv

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📝 Efficiently Robustify Pre-Trained Models 🔭

"Involves robustifying smaller models, at a lower computation cost, and then use them as teachers to tune a fraction of these large scale networks, reducing the overall computational overhead." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07499v1 #arxiv

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📝 DePT: Decoupled Prompt Tuning 🔭

"The DePT framework decouples base-specific knowledge from feature channels into an isolated feature space during prompt tuning, which preserves task-shared knowledge in the original feature space for achieving better generalization on new tasks." [gal30b+] 🤖 #CV

⚙ https://github.com/Koorye/DePT
🔗 https://arxiv.org/abs/2309.07439v1 #arxiv

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📝 Physical Invisible Backdoor Based on Camera Imaging 🔭

"By using the camera imaging process and leveraging the CFA interpolation algorithm and the camera fingerprint feature, the proposed method can implement a physical invisible backdoor attack without changing nature pixels of the images." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07428v1 #arxiv

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📝 Masked Diffusion with Task-Awareness for Procedure Planning in Instructional Videos 🔭

"The introduced mask acts akin to a task-oriented attention filter, enabling the diffusion/denoising process to concentrate on a subset of action types that are pertinent to the given task." [gal30b+] 🤖 #CV

⚙ https://github.com/ffzzy840304/Masked-PDPP
🔗 https://arxiv.org/abs/2309.07409v1 #arxiv

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📝 Flexible Visual Recognition by Evidential Modeling of Confusion and Ignorance 🔭

"By estimating Dirichlet concentration parameters for singletons, comprehensive subjective opinions, including confusion and ignorance, could be achieved via further evidence combinations, which enables flexible visual recognition with uncertainty quantification." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07403v1 #arxiv

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📝 Judging a Video by Its Bitstream Cover 🔭

"Develops a bitstream-based classifier that uses the MPEG-1/2/4 bitstream of a video to classify them into distinct categories such as Sport, Music Video, and Animation." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07361v1 #arxiv

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📝 Multi-Modal Hybrid Learning and Sequential Training for RGB-T Saliency Detection 🔭

"MMHL is comprised of supervised and self-supervised loss functions which utilize semantic features from different modalities and reduce the distance between RGB and thermal features, respectively, during saliency map generation." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07297v1 #arxiv

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📝 Unbiased Face Synthesis with Diffusion Models: Are We There Yet? 🔭🧠

"Consists of several qualitative and quantitative measures, including embedding-based metrics and user studies, to audit the characteristics of generated faces conditioned on a set of social attributes." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.07277v1 #arxiv

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📝 LCReg: Long-Tailed Image Classification with Latent Categories Based Recognition 🔭

"Learns a set of class-agnostic latent features shared by both head and tail classes, and then use semantic data augmentation on the latent features to implicitly increase the diversity of the training sample." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.07186v1 #arxiv

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📝 Boosting Unsupervised Contrastive Learning Using Diffusion-Based Data Augmentation From Scratch 🧠🔭

"DiffAug first mines sufficient prior semantic knowledge about the neighborhood to guide the diffusion steps, eliminating the need for labels, external data/models, or prior knowledge, while ensuring that the augmented and original data share a smoothed latent space." [gal30b+] 🤖 #LG #CE #CV

⚙ https://github.com/zangzelin/DiffAug
🔗 https://arxiv.org/abs/2309.07909v1 #arxiv

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📝 Hardening RGB-D Object Recognition Systems Against Adversarial Patch Attacks 🔭

"Finds that RGB features make the functions learned by the network more complex and, thus, more sensitive to small perturbations, compared to depth features, which have been proved to be more invariant to small transformations." [gal30b+] 🤖 #CV #CR

⚙ https://github.com/Trusted-AI/adversarial-robustness-toolbox/
🔗 https://arxiv.org/abs/2309.07106v1 #arxiv

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📝 FAIR: Frequency-Aware Image Restoration for Industrial Visual Anomaly Detection 🔭

"FAIR is a novel self-supervised image restoration task that restores images from their high-frequency components, which enables precise reconstruction of normal patterns while mitigating unfavorable generalization to anomalies." [gal30b+] 🤖 #CV

⚙ https://github.com/liutongkun/FAIR
🔗 https://arxiv.org/abs/2309.07068v1 #arxiv

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📝 Aggregating Long-Term Sharp Features via Hybrid Transformers for Video Deblurring 🔭

"A window-based local Transformer is employed for exploiting features from neighboring frames with cross attention, and a global multi-scale Transformer is utilized to aggregate long-term sharp features." [gal30b+] 🤖 #CV

⚙ https://github.com/shangwei5/STGTN
🔗 https://arxiv.org/abs/2309.07054v1 #arxiv

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📝 Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning 🔭

"A margin-based Prototypical Contrast Learning embedding network reaps the benefits of prototype-data (cluster quality enhancement) and implicit data-data (fine-grained representations) interaction while providing substantial cluster supervision to the embedding network and the generator." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06987v1 #arxiv

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📝 Differentiable JPEG: The Devil Is in the Details 🔭

"A novel differentiable JPEG approach is proposed, overcoming the limitations of existing methods: Differentiable wrt the input image, the JPEG quality, the quantization tables, and the color conversion parameters." [gal30b+] 🤖 #CV #MM

⚙ https://github.com/necla-ml/Diff-JPEG
🔗 https://arxiv.org/abs/2309.06978v1 #arxiv

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📝 Neural Network-Based Coronary Dominance Classification of RCA Angiograms 🔭

"Employs convolutional neural network ConvNext and Swin transformer for 2D image (frames) classification along with a majority vote for cardio angiographic view classification." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06958v1 #arxiv

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📝 DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models 🔭

"DreamStyler optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality and flexibility to accommodate a range of style references." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06933v1 #arxiv

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📝 Hydra: Multi-Head Low-Rank Adaptation for Parameter Efficient Fine-Tuning 🔭

"Proposes a multi-branch adaption method named Hydra for fine-tuning large language models, which leverages both parallel and sequential adaptation methods simultaneously to combine the benefits of both." [gal30b+] 🤖 #CV

⚙ https://github.com/extremebird/Hydra
🔗 https://arxiv.org/abs/2309.06922v1 #arxiv

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📝 Keep It SimPool: Who Said Supervised Transformers Suffer From Attention Deficit? 🔭🧠

"Simpool replaces global average pooling by spatial similarity attention to improve performance and provide attention maps delineating object boundaries without explicit losses or modifying the architecture, whether supervised or self-supervised." [gal30b+] 🤖 #CV #LG

⚙ https://github.com/billpsomas/simpool
🔗 https://arxiv.org/abs/2309.06891v1 #arxiv

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📝 Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization 🔭

"We disentangle high-dimensional video features into multiple components, which are explicitly trained to encode non-overlapping semantics with mutual information maximization loss (MIM) to ensure that useful task-relevant information is extracted from the original features." [gal30b+] 🤖 #CV #MM

⚙ https://github.com/yyyooooo/DMI/
🔗 https://arxiv.org/abs/2309.06877v1 #arxiv

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📝 TAP: Targeted Prompting for Task Adaptive Generation of Textual Training Instances for Visual Classification 🔭

"Works by training a linear classifier on text representations generated by a pre-trained language model (GPT-3), conditioned by task-specific prompts." [gal30b+] 🤖 #CV

⚙ https://github.com/jmiemirza/TAP
🔗 https://arxiv.org/abs/2309.06809v1 #arxiv

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📝 Motion-Bias-Free Feature-Based SLAM 🔭

"Proposes a set of modifications that remedy the motion bias problem in SLAM by improving the feature matching process, the data association, and the outlier rejection of the pose-graph optimizer." [gal30b+] 🤖 #CV

⚙ https://github.com/alejandrofontan/ORB_SLAM2_Deterministic
🔗 https://arxiv.org/abs/2309.06792v1 #arxiv

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📝 Remote Sensing Object Detection Meets Deep Learning: A Meta-Review of Challenges and Advances 🔭

"This review summarizes the development process of RSOD and identifies five main challenges in RSOD including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06751v1 #arxiv

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📝 GelFlow: Self-Supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement 🔭

"Employs a multi-scale feature pyramid to handle large deformations by constructing a multi-scale feature pyramid from the input image, which is more suitable for vision-based tactile sensor images." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06735v1 #arxiv

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📝 Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense 🔭🧠

"Works by using a non-parametric deep network to resemble the physical equations behind the image formation, such as denoising, super-resolution, inpainting, and flash, etc." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.06724v1 #arxiv

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📝 Deep Attentive Time Warping 🔭

"Predicts all local correspondences between two time series based on metric learning and is trained based on metric learning, which enables it to learn the optimal data-dependent warping for the target task." [gal30b+] 🤖 #CV

⚙ https://github.com/matsuo-shinnosuke/deep-attentive-time
🔗 https://arxiv.org/abs/2309.06720v1 #arxiv

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📝 MPI-Flow: Learning Realistic Optical Flow with Multiplane Images 🔭

"A learning-based MPI-Flow framework is proposed, which generates highly realistic optical flow maps from real-world images and achieves state-of-the-art performance in both unsupervised and supervised learning of optical flow estimation models." [gal30b+] 🤖 #CV

⚙ https://github.com/Sharpiless/MPI-Flow
🔗 https://arxiv.org/abs/2309.06714v1 #arxiv

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📝 ShaDocFormer: A Shadow-Attentive Threshold Detector with Cascaded Fusion Refiner for Document Shadow Removal' to the ICASSP 2024 Online Submission System 🔭

"The architecture of ShaDocFormer includes Shadow-attentive Threshold Detector (STD) and Cascaded Fusion Refiner (CFR), where CFR takes advantage of STD to generate shadow mask." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06670v1 #arxiv

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📝 Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity 🔭🧠

"Introduces a training procedure that enables the exploitation of activation sparsity by inducing semi-structured sparsity through regularization and a training procedure that is aware of the structure of the GEMM operation." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.06626v1 #arxiv

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📝 Zero-Shot Visual Classification with Guided Cropping 🔭

"Uses a zero-shot object detector to guide cropping of input images and increase the influence of object-relevant features in zero-shot classification task using CLIP." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06581v1 #arxiv

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📝 Strong-Weak Integrated Semi-Supervision for Unsupervised Single and Multi Target Domain Adaptation 🔭

"A strong representative set with high confidence but low diversity target domain samples and a weak representative set with low confidence but high diversity target domain samples are generated and updated dynamically during training." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06528v1 #arxiv

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📝 DF-TransFusion: Multimodal Deepfake Detection via Lip-Audio Cross-Attention and Facial Self-Attention 🔭

"Presents a novel multi-modal audio-video framework designed to concurrently process audio and video inputs for deepfake detection tasks that leverages the synergy between the two modalities." [gal30b+] 🤖 #CV #MM

🔗 https://arxiv.org/abs/2309.06511v1 #arxiv

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📝 PILOT: A Pre-Trained Model-Based Continual Learning Toolbox 🧠🔭

"PILOT implements several state-of-the-art pre-trained model-based approaches that tackle class-incremental learning, which is a continual learning setting where new classes continually arrive." [gal30b+] 🤖 #LG #CV

🔗 https://arxiv.org/abs/2309.07117v1 #arxiv

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📝 Generalizable Neural Fields as Partially Observed Neural Processes 🧠🔭

"Proposes a new paradigm that views the large-scale training of neural representations as a part of a partially-observed neural process framework, and leverage neural process algorithms to solve this task." [gal30b+] 🤖 #LG #CV

🔗 https://arxiv.org/abs/2309.06660v1 #arxiv

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📝 Harmonic-Nas: Hardware-Aware Multimodal Neural Architecture Search on Resource-Constrained Devices 🧠🔭

"Harmonic-NAS is a two-tier Neural Architecture Search (NAS) approach for Multimodal Neural Networks (MM-NN) for efficient inference on IoT devices." [gal30b+] 🤖 #LG #CV

🔗 https://arxiv.org/abs/2309.06612v1 #arxiv

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📝 Exploring Non-Additive Randomness on ViT Against Query-Based Black-Box Attacks 🔭

"Proposes a novel approach of using non-additive stochasticity in Vision Transformers based models to defend against black-box attacks in the query-based scenario, which is underexplored to date." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06438v1 #arxiv

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📝 Padding-Free Convolution Based on Preservation of Differential Characteristics of Kernels 🔭

"By making convolution over an incomplete sliding window "collapse" to a linear differential operator evaluated locally at its central pixel, which no longer requires information from the neighbouring missing pixels." [gal30b+] 🤖 #CV

⚙ https://github.com/stfc-sciml/DifferentialConv2d
🔗 https://arxiv.org/abs/2309.06370v1 #arxiv

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📝 Exploring Flat Minima for Domain Generalization with Large Learning Rates 🔭

"Observes that using a large learning rate can not only promote weight diversify but also help identify flat regions in the loss landscape, which can be used to improve the generalization of DNNs." [gal30b+] 🤖 #CV

⚙ https://github.com/koncle/DG-with-Large-LR
🔗 https://arxiv.org/abs/2309.06337v1 #arxiv

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📝 SAMPLING: Scene-Adaptive Hierarchical Multiplane Images Representation for Novel View Synthesis From a Single Image 🔭

"Introduces SAMPLING, a novel view synthesis method for large-scale outdoor scenes with a single image as input and an adaptive-bins strategy for multiplane images." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06323v1 #arxiv

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📝 Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data 🔭

"Proposes a three-stage network to remove specular highlights from a single image by decomposing it into the albedo, shading, and specular residue components, refining the decomposition results, and adjusting tone of the refined result to match that of the input as closely as possible." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06302v1 #arxiv

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📝 Self-Training and Multi-Task Learning for Limited Data: Evaluation Study on Object Detection 🔭

"Self-training and multi-task learning frameworks, despite being particularly data demanding, have potentials for data exploitation if such assumptions can be relaxed to be less restrictive and data demanding." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06288v1 #arxiv

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📝 Fg-T2m: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model 🔭

"Contains two parts: 1) a linguistics-structure assisted module that constructs accurate and complete language feature to fully utilize text information; and 2) a context-aware progressive reasoning module that learns neighborhood and overall semantic linguistics features from shallow and deep graph neural networks." [gal30b+] 🤖 #CV #MM

🔗 https://arxiv.org/abs/2309.06284v1 #arxiv

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📝 IBAFormer: Intra-Batch Attention Transformer for Domain Generalized Semantic Segmentation 🔭

"Proposes a novel intra-batch attention mechanism, which incorporates information from other independent samples in the same batch, enriching contextual information and diversifying training data for each attention block." [gal30b+] 🤖 #CV

⚙ https://github.com/open-mmlab/mmsegmentation
🔗 https://arxiv.org/abs/2309.06282v1 #arxiv

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📝 OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation 🔭

"Proposes an unsupervised framework of object-centric temporal action segmentation (OTAS), which consists of three modules as shown in (Figure ): global feature extraction, self-supervised local feature extraction, and boundary selection." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06276v1 #arxiv

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📝 Modality Unifying Network for Visible-Infrared Person Re-Identification 🔭

"A Modality Unifying Network (MUN) is proposed for cross-modality person search by generating an auxiliary modality to explore modality-shared and modality-specific representations." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06262v1 #arxiv

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📝 Use Neural Networks to Recognize Students' Handwritten Letters and Incorrect Symbols 🔭

"Given students' multiple-choice answers as the input, the image classifier predicts their answers, i-e, the classification label with the highest predicted probability is considered the correct answer." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06221v1 #arxiv

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📝 Fast Sparse PCA via Positive Semidefinite Projection for Unsupervised Feature Selection 🔭

"By imposing PSD constraint and a regularization parameter setting strategy, it's proved that the optimal solution to a convex SPCA model is optimized on the PSD cone, which is equivalent to an orthogonal matrix." [gal30b+] 🤖 #CV

⚙ https://github.com/liuyanfang023/KBS-RNE
🔗 https://arxiv.org/abs/2309.06202v1 #arxiv

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📝 Dual-Path Temporal Map Optimization for Make-Up Temporal Video Grounding 🔭

"DPTMO extracts both query-agnostic and query-guided features to construct two proposal sets and uses specific evaluation methods for the two sets, which represent the cross-modal makeup video-text similarity and multi-modal fusion relationship, complementing each other." [gal30b+] 🤖 #CV #MM

⚙ https://github.com/AIM3-RUC/Youmakeup
🔗 https://arxiv.org/abs/2309.06176v1 #arxiv

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📝 Towards Reliable Domain Generalization: A New Dataset and Evaluations 🔭🧠

"Proposes a new domain generalization task for handwritten Chinese character recognition (HCCR) to enrich the application scenarios of DG method research, which is not studied in previous work." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.06142v1 #arxiv

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📝 Dynamic Visual Prompt Tuning for Parameter Efficient Transfer Learning 🔭

"DVPT is a new PETL method, it can generate a dynamic instance-wise token for each image via a Meta-Net module, which captures the dynamic instance-wise visual features." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06123v1 #arxiv

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📝 C-RITNet: Set Infrared and Visible Image Fusion Free From Complementary Information Mining 🔭

"First skillfully sidesteps aggregating complementary information in IVIF, and then it reasonably transfers complementary information into redundant one to integrate both the shared and complementary features from two modalities." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.06118v1 #arxiv

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📝 Learning From History: Task-Agnostic Model Contrastive Learning for Image Restoration 🔭

"SPNIR introduces the Self-Prior guided Negative loss to enable "learning from history", which can adaptively and automatically generate negative samples to train the target model without introducing any task-specific bias." [gal30b+] 🤖 #CV

⚙ https://github.com/Aitical/Task-agnostic_Model_Contrastive_Learning_Image_Restoration
🔗 https://arxiv.org/abs/2309.06023v1 #arxiv

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📝 ATTA: Anomaly-Aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation 🔭🧠

"Proposes the dual-level OOD detection approach to handle domain shift and semantic shift jointly, by distinguishing whether domain shift exists in the image by leveraging global low-level features, and identifying pixels with semantic shift by utilizing dense high-level feature maps." [gal30b+] 🤖 #CV #LG

⚙ https://github.com/gaozhitong/ATTA
🔗 https://arxiv.org/abs/2309.05994v1 #arxiv

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📝 Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentation 🔭

"A straightforward textual template and a foreground-background segmentation algorithm are employed to generate foreground images set against isolated backgrounds, while an image captioning method and a text-to-image synthesis framework are used to produce context images." [gal30b+] 🤖 #CV

⚙ https://github.com/gyhandy/Text2Image-for-Detection
🔗 https://arxiv.org/abs/2309.05956v1 #arxiv

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📝 Hierarchical Conditional Semi-Paired Image-to-Image Translation for Multi-Task Image Defect Correction on Shopping Websites 🔭🧠

"A novel unified Image-to-Image model to correct multiple defects across different product types, leveraging an attention mechanism to hierarchically incorporate high-level defect groups and specific defect types to guide the network to focus on defect-related image regions." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.05883v1 #arxiv

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📝 Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation 🔭

"The Self-Correlation and Cross-Correlation Learning Network (SCCNe) is a few-shot remote sensing image semantic segmentation model that consists of a support branch and a query branch." [gal30b+] 🤖 #CV

⚙ https://github.com/linhanwang/SCCNe
🔗 https://arxiv.org/abs/2309.05840v1 #arxiv

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📝 SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-Supervised Skeleton-Based Action Recognition 🔭

"SCD-Net is based on a novel contrastive learning framework which learns disentangled spatial and temporal clues via a constructed anchor and the proposed masking strategy with structural constraints, and can be applied for skeleton-based action recognition." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.05834v1 #arxiv

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📝 Blendshapes GHUM: Real-Time Monocular Facial Blendshape Prediction 🔭

"Presents Blendshapes GHUM, an on-device ML pipeline that predicts 52 facial blendshape coefficients at 30+ FPS on modern mobile phones, from a single monocular RGB image and enables facial motion capture applications like virtual avatars." [gal30b+] 🤖 #CV

⚙ https://github.com/google/
🔗 https://arxiv.org/abs/2309.05782v1 #arxiv

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