📝 TransferDoc: A Self-Supervised Transferable Document Representation Learning Model Unifying Vision and Language 🔭

"TransferDoc is a cross-modal transformer-based architecture pre-trained in a self-supervised fashion using three novel pretext objectives which learns richer semantic concepts by unifying language and visual representations." [gal30b+] 🤖 #CV

⚙ https://github.com/tesseract-ocr/tesseract
🔗 https://arxiv.org/abs/2309.05756v1 #arxiv

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📝 InstaFlow: One Step Is Enough for High-Quality Diffusion-Based Text-to-Image Generation 🧠🔭

"InstaFlow is trained via a novel text-conditioned pipeline, in which reflow plays a critical role in improving the assignment between noise and images by refining the coupling between noises and images through straightening the trajectories of probability flows." [gal30b+] 🤖 #LG #CV

⚙ https://github.com/gnobitab/InstaFlow
🔗 https://arxiv.org/abs/2309.06380v1 #arxiv

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📝 Elucidating the Solution Space of Extended Reverse-Time SDE for Diffusion Models 🧠🔭

"Formulates the sampling process as an extended reverse-time SDE (ER SDE) and devise fast and training-free samplers, ER-SDE Solvers, elevating the efficiency of stochastic samplers to unprecedented levels." [gal30b+] 🤖 #LG #CV

🔗 https://arxiv.org/abs/2309.06169v1 #arxiv

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📝 Certified Robust Models with Slack Control and Large Lipschitz Constants 🧠🔭

"Proposes a Calibrated Lipschitz-Margin Loss (CLL) that improves certified robustness by explicitly calibrating the loss wrt margin and Lipschitz constant, thereby establishing full control over slack and improving robustness certificates even with larger Lipschitz constants." [gal30b+] 🤖 #LG #CV

⚙ https://github.com/mlosch/CLL
🔗 https://arxiv.org/abs/2309.06166v1 #arxiv

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📝 Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning 🧠🔭

"An expert network is relieved of the duty of keeping the previous knowledge and can focus on performing optimally on the new tasks, while a previous network is used in an adaptation-retrospection phase to avoid forgetting and initialize a new expert with the knowledge of the old network." [gal30b+] 🤖 #LG #CV

⚙ https://github.com/alviur/pocon_wacv2024
🔗 https://arxiv.org/abs/2309.06086v1 #arxiv

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📝 KD-FixMatch: Knowledge Distillation Siamese Neural Networks 🧠🔭

"Presents KD-FixMatch, a novel SSL algorithm that addresses the limitations of FixMatch by incorporating knowledge distillation to enhance performance and reduce performance degradation in the early training stage." [gal30b+] 🤖 #LG #CV

⚙ https://github.com/petewarden/tensorflow_
🔗 https://arxiv.org/abs/2309.05826v1 #arxiv

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📝 DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices 🔭

"Proposes a collaborative inference framework, DeViT, to facilitate edge deployment by decomposing large ViTs into multiple small models for collaborative inference at the edge devices." [gal30b+] 🤖 #CV #DC #PF

🔗 https://arxiv.org/abs/2309.05015v1 #arxiv

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📝 Towards Fully Decoupled End-to-End Person Search 🔭

"Task-incremental end-to-end person search network is proposed for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.04967v1 #arxiv

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📝 Semi-Supervised Learning for Face Anti-Spoofing Using Apex Frame 🔭

"An apex frame is derived from a video by computing a weighted sum of its frames, where the weights are determined using a Gaussian distribution centered around the video's central frame." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.04958v1 #arxiv

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📝 Semi-Supervised Instance Segmentation with a Learned Shape Prior 🔭

"A variational autoencoder (VAE) is trained on either (a) real cell shape patches or (b) synthetic cell shape patches + noise to learn shape prior." [gal30b+] 🤖 #CV

⚙ https://github.com/looooongChen/shape
🔗 https://arxiv.org/abs/2309.04888v1 #arxiv

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📝 SortedAP: Rethinking Evaluation Metrics for Instance Segmentation 🔭

"Proposes a new metric called sortedAP, which strictly decreases with both object- and pixel-level imperfections and has an uninterrupted penalization scale over the entire domain." [gal30b+] 🤖 #CV

⚙ https://www.github.com/looooongChen/sortedAP
🔗 https://arxiv.org/abs/2309.04887v1 #arxiv

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📝 ABC Easy as 123: A Blind Counter for Exemplar-Free Multi-Class Class-Agnostic Counting 🔭🧠

"Composed by three stages: a) a feature extractor, b) an object detector, and c) a blind counter that predicts the number of each type of object in the image without using any example of that type." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.04820v1 #arxiv

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📝 Speech2Lip: High-Fidelity Speech to Lip Generation by Learning From a Short Video 🔭

"A decomposition-synthesis-composition framework that disentangles speech-sensitive and speech-insensitive motion/appearance to facilitate effective learning from limited training data, resulting in the generation of natural-looking videos." [gal30b+] 🤖 #CV

⚙ https://github.com/CVMI-Lab/Speech2Lip
🔗 https://arxiv.org/abs/2309.04814v1 #arxiv

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📝 Self-Supervised Transformer with Domain Adaptive Reconstruction for General Face Forgery Video Detection 🔭

"Explores to take full advantage of the difference between real and forgery videos by only exploring the common representation of real face videos in a self-supervised manner, and then fine-tuned a linear head on specific face forgery video datasets." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.04795v1 #arxiv

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📝 Latent Degradation Representation Constraint for Single Image Deraining 🔭

"The DAEncoder is proposed to adaptively extract latent degradation representation by using the deformable convolutions to exploit the direction consistency of rain streaks, then the constraint loss is introduced to explicitly constrain the degradation representation learning during training." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.04780v1 #arxiv

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📝 Deep Video Restoration for Under-Display Camera 🔭

"Consists of a spatial branch with local-aware transformers, a temporal branch embedded temporal transformers, and a spatial-temporal fusion module (see Fig." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.04752v1 #arxiv

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📝 Grouping Boundary Proposals for Fast Interactive Image Segmentation 🔭

"The adaptive cut can disconnect the image domain such that the target contours are imposed to pass through this cut only once, and the selected boundary proposals and corresponding minimal paths are used to delineate the target contours." [gal30b+] 🤖 #CV

⚙ https://github.com/Mirebeau/HamiltonFastMarching
🔗 https://arxiv.org/abs/2309.04169v1 #arxiv

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📝 Multimodal Transformer for Material Segmentation 🔭🧠

"Proposes a fusion strategy that can effectively fuse information from different combinations of multiple modalities including RGB, Angle of Linear Polarization (AoLP), Degree of Linear Polarization (DoLP) and Near-Infrared (NIR)." [gal30b+] 🤖 #CV #LG

⚙ https://github.com/csiplab/MMSFormer
🔗 https://arxiv.org/abs/2309.04001v1 #arxiv

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📝 Adapting Self-Supervised Representations to Multi-Domain Setups 🔭🧠

"The Domain Disentanglement Module (DDM) is a lightweight component that can be used with various self-supervised learning methods to improve their ability to learn generalizable representations when trained on multiple domains." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.03999v1 #arxiv

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📝 CDFSL-V: Cross-Domain Few-Shot Learning for Videos 🔭

"Leverages a masked autoencoder-based self-supervised training objective to learn from both source and target data in a self-supervised manner, which can be used to learn generic features from target video data." [gal30b+] 🤖 #CV

⚙ https://github.com/Sarinda251/CDFSL-V}{https://github.com/Sarinda251/CDFSL-V
🔗 https://arxiv.org/abs/2309.03989v1 #arxiv

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📝 UER: A Heuristic Bias Addressing Approach for Online Continual Learning 🧠🔭

"UER learns current samples only by the angle factor and further replays previous samples by both the norm and angle factors to address the bias problem in continual learning, achieving superior performance over various state-of-the-art methods." [gal30b+] 🤖 #LG #CV

⚙ https://github.com/FelixHuiweiLin/UER
🔗 https://arxiv.org/abs/2309.04081v1 #arxiv

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📝 Improving Resnet-9 Generalization Trained on Small Datasets 🧠🔭

"A combination of various techniques to improve generalization including sharpness aware optimization, label smoothing, gradient centralization, input patch whitening as well as metalearning based training." [gal30b+] 🤖 #LG #CV

🔗 https://arxiv.org/abs/2309.03965v1 #arxiv

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📝 Exploring Sparse MoE in GANs for Text-Conditioned Image Synthesis 🔭

"A mixture-of-experts (MoE) based generative text-to-image (T2I) model that employs a collection of experts to process the feature, together with a sparse router to help select the most suitable expert for each feature point." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03904v1 #arxiv

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📝 Tracking Anything with Decoupled Video Segmentation 🔭

"Works by first using a segmentation network on every frame, and the network produces a probability for each pixel to belong to the foreground object or background; then it uses bi-directional propagation for (semi-)online fusion of segmentation hypotheses from different frames to generate a coherent segmentation." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03903v1 #arxiv

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📝 The Making and Breaking of Camouflage 🔭

"Proposes three camouflage scores for measuring camouflage in the feature space, which are used to evaluate existing camouflage datasets and generate a large-scale and challenging dataset for camouflaged instance segmentation." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03899v1 #arxiv

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📝 ProPainter: Improving Propagation and Transformer for Video Inpainting 🔭

"Introduces a novel video inpainting framework called ProPainter, which involves enhanced propagation mechanism and sparse Transformer for efficient video inpainting, outperforming previous state-of-the-art approaches by a large margin." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03897v1 #arxiv

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📝 InstructDiffusion: A Generalist Modeling Interface for Vision Tasks 🔭

"Formulates human instructions to a pixel prediction task, where an InstructDiffusion model is trained to predict pixels according to user instructions, such as encircling the man's left shoulder in red or applying a blue mask to the left car." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03895v1 #arxiv

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📝 Box-Based Refinement for Weakly Supervised and Unsupervised Localization Tasks 🔭

"A box-based detector is trained to predict the location of the phrases in the image, and then applied to the output of the network to improve it further and enhance the localization performance of weakly supervised and unsupervised methods." [gal30b+] 🤖 #CV

⚙ https://github.com/eyalgomel/box-based-refinement
🔗 https://arxiv.org/abs/2309.03874v1 #arxiv

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📝 Text-to-Feature Diffusion for Audio-Visual Few-Shot Learning 🔭

"AV-DIFF is a text-to-feature diffusion framework, which first fuses the temporal and audio-visual features via cross-modal attention and then generates multi-modal features for the novel classes." [gal30b+] 🤖 #CV

⚙ https://github.com/ExplainableML/AVDIFF-GFSL
🔗 https://arxiv.org/abs/2309.03869v1 #arxiv

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📝 Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption 🔭

"A novel phasic content fusing few-shot diffusion model with directional distribution consistency loss, which targets different learning objectives at distinct training stages of the diffusion model, is designed." [gal30b+] 🤖 #CV

⚙ https://github.com/sjtuplayer/few-shot-diffusion
🔗 https://arxiv.org/abs/2309.03729v1 #arxiv

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📝 Interpretable Visual Question Answering via Reasoning Supervision 🔭

"Based on a transformer-based architecture that leverages reasoning supervision as a supervisory signal to guide the visual attention to important elements of the scene, without requiring explicit grounding annotations." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03726v1 #arxiv

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📝 Efficient Adaptive Human-Object Interaction Detection with Concept-Guided Memory 🔭

"ADA-CM has two operating modes: (1) training-free and (2) updating a lightweight set of parameters, which can be incorporated with existing HOI detectors." [gal30b+] 🤖 #CV

⚙ https://github.com/ltttpku/ADA-CM
🔗 https://arxiv.org/abs/2309.03696v1 #arxiv

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📝 Prompt-Based Context- And Domain-Aware Pretraining for Vision and Language Navigation 🔭

"PANDA consists of a domain-aware stage and a context-aware stage, which performs prompt-based tuning and contrastive learning, respectively, on a pretrained VLN model." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03661v1 #arxiv

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📝 Enhancing Sample Utilization Through Sample Adaptive Augmentation in Semi-Supervised Learning 🔭

"Sample Adaptive Augmentation(SAA) consists of a sample selection module and a sample augmentation module, which helps to optimize the SSL models by giving more attention to naive samples and augmenting them in a more diverse manner." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03598v1 #arxiv

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📝 DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions 🔭

"Learns to classify the actual position for each non-overlapping patch among all possible positions solely based on their visual appearance, by minimizing the negative log-likelihood." [gal30b+] 🤖 #CV

⚙ https://github.com/Haochen-Wang409/DropPos
🔗 https://arxiv.org/abs/2309.03576v1 #arxiv

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📝 Region Generation and Assessment Network for Occluded Person Re-Identification 🔭

"RGANet utilizes pre-trained CLIP to locate the human body regions using semantic prototypes extracted from text descriptions, and then it measures the importance of each generated region." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03558v1 #arxiv

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📝 Trash to Treasure: Low-Light Object Detection via Decomposition-and-Aggregation 🔭

"A newly designed enhancer is introduced as the scene decomposition module, whose removed illumination is exploited as the auxiliary to extract detection-friendly features, and then a semantic aggregation module is established to further integrate multi-scale scene-related semantic information." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03548v1 #arxiv

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📝 Dynamic Frame Interpolation in Wavelet Domain 🔭

"WaveletVFI uses a lightweight motion perception network to estimate an initial intermediate optical flow, and embeds a threshold classifier in it to learn a dynamic threshold for more computation reduction." [gal30b+] 🤖 #CV

⚙ https://github.com/ltkong218/WaveletVFI
🔗 https://arxiv.org/abs/2309.03508v1 #arxiv

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📝 DetermiNet: A Large-Scale Diagnostic Dataset for Complex Visually-Grounded Referencing Using Determiners 🔭

"DetermiNet provides 250,000 synthetically generated images and captions with ground truth bounding boxes for the objects of interest in images based on 25 determiners." [gal30b+] 🤖 #CV

⚙ https://github.com/clarence-lee-sheng/
🔗 https://arxiv.org/abs/2309.03483v1 #arxiv

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📝 Temporal Collection and Distribution for Referring Video Object Segmentation 🔭

"Given a video sequence, the proposed framework simultaneously maintains a global referent token and a sequence of object queries across the frames, where the former is responsible for capturing video-level referent according to the language expression, while the latter serves to better locate and segment objects with each frame." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03473v1 #arxiv

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📝 Perceptual Quality Assessment of 360$^\circ$ Images Based on Generative Scanpath Representation 🔭

"The proposed generative scanpath representation (GSR), which aggregates varied perceptual experiences of multi-hypothesis users under a predefined viewing condition, provides a global overview of gazed-focused contents derived from scanpaths." [gal30b+] 🤖 #CV

⚙ https://github.com/xiangjieSui/GSR
🔗 https://arxiv.org/abs/2309.03472v1 #arxiv

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📝 Multi-Modality Guidance Network for Missing Modality Inference 🔭🧠

"Proposes a novel guidance network that promotes knowledge sharing during training, taking advantage of the multimodal representations to train better single-modality models for inference on scenarios with missing modalities." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.03452v1 #arxiv

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📝 Distribution-Aware Prompt Tuning for Vision-Language Models 🔭

"Distribution-aware prompt tuning maximizes inter-dispersion as well as minimizing intra-dispersion between embeddings of two modalities in the latent space, which leads to effective feature space alignment between them." [gal30b+] 🤖 #CV

⚙ https://github.com/mlvlab/DAPT
🔗 https://arxiv.org/abs/2309.03406v1 #arxiv

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📝 Reasonable Anomaly Detection in Long Sequences 🔭

"A Stacked State Machine model is proposed to represent the temporal dependencies which are consistent across long-range observations and functions in predicting future states based on past ones, the divergence between the predictions with inherent normal patterns and observed ones determines anomalies." [gal30b+] 🤖 #CV

⚙ https://github.com/AllenYLJiang/Anomaly-Detection-in-Sequences
🔗 https://arxiv.org/abs/2309.03401v1 #arxiv

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📝 Active Shooter Detection and Robust Tracking Utilizing Supplemental Synthetic Data 🔭

"Uses domain randomization and transfer learning to allow for the effective training of YOLOv8 using synthetic data generated with Unreal Engine, which is then used to detect shooters in video streams." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03381v1 #arxiv

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📝 ViewMix: Augmentation for Robust Representation in Self-Supervised Learning 🔭🧠

"Cut and paste patches from one view to another and create different views of the same image to form positive pairs, and the network is trained to maximize the agreement between positive pairs while minimizing the agreement between negative pairs." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.03360v1 #arxiv

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📝 Source Camera Identification and Detection in Digital Videos Through Blind Forensics 🔭🧠

"Based on feature extraction from the video frames followed by feature selection and classification of the extracted features to identify the source (camera) from which the video was captured." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2309.03353v1 #arxiv

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📝 Relay Diffusion: Unifying Diffusion Process Across Resolutions for Image Synthesis 🔭🧠

"Relay Diffusion Model (RDM) transfers a low-resolution image or noise into an equivalent high-resolution one for diffusion model via blurring diffusion and block noise." [gal30b+] 🤖 #CV #LG

⚙ https://github.com/THUDM/RelayDiffusion
🔗 https://arxiv.org/abs/2309.03350v1 #arxiv

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📝 Robust Visual Tracking by Motion Analyzing 🔭

"Works by modeling the motion pattern using a tensor structure, obtained through Tucker2 decomposition, which is effective in describing the target's motion by analyzing the correlation between adjacent frames." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03247v1 #arxiv

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📝 RepSGG: Novel Representations of Entities and Relationships for Scene Graph Generation 🔭

"RepSGG learns to sample semantically discriminative and representative points for relationship inference, formulating a subject as queries, an object as keys, and their relationship as the maximum attention weight between pairwise queries and keys." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03240v1 #arxiv

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📝 A Multisensor Hyperspectral Benchmark Dataset for Unmixing of Intimate Mixtures 🔭

"Generated using a set of different hyperspectral sensors to capture the reflectance spectra in the visible, near, short, mid, and long-wavelength infrared regions (350-15385) nm." [gal30b+] 🤖 #CV

⚙ https://github.com/VisionlabUA/Multisensor_datasets
🔗 https://arxiv.org/abs/2309.03216v1 #arxiv

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📝 Better Practices for Domain Adaptation 🧠🔭

"Analyzes state of domain adaptation (DA) in a rigorous way by benchmarking a suite of validation criteria with different DA algorithms including Unsupervised Domain Adaptation (UDA), Source-Free Domain Adaptation (SFDA), and Test Time Adaptation (TTA)." [gal30b+] 🤖 #LG #CV

🔗 https://arxiv.org/abs/2309.03879v1 #arxiv

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📝 PDiscoNet: Semantically Consistent Part Discovery for Fine-Grained Recognition 🔭

"Proposes an end-to-end trainable method to discover object parts by using only image-level class labels along with priors encouraging the parts to be discriminative, compact, distinct from each other, equivariant to rigid transforms, and active in at least some of the images." [gal30b+] 🤖 #CV

⚙ https://github.com/robertdvdk/part_detection
🔗 https://arxiv.org/abs/2309.03173v1 #arxiv

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📝 Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration 🔭

"Uses IoU-Aware calibration, a conditional version of Beta calibration to implicitly account for the likelihood of each detection being a duplicate and adjust the confidence score accordingly." [gal30b+] 🤖 #CV

⚙ https://github.com/Blueblue4/IoU-AwareCalibration}{code
🔗 https://arxiv.org/abs/2309.03110v1 #arxiv

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📝 Prompt-Based All-in-One Image Restoration Using CNNs and Transformer 🔭

"Our network architecture is based on a CNN-transformer backbone, the CAPTNet, which consists of multiple CAPT blocks, the multi-scale information can be aggregated through the CAPTNet." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03063v1 #arxiv

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📝 Adaptive Growth: Real-Time CNN Layer Expansion 🔭

"Iteratively introduces kernels to the convolutional layer, gauging its real-time response to varying data to guide its growth and improving the model's performance and scalability." [gal30b+] 🤖 #CV

⚙ https://github.com/YunjieZhu/Extensible-Convolutional-Layer-git-version
🔗 https://arxiv.org/abs/2309.03049v1 #arxiv

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📝 McM: Multi-Condition Motion Synthesis Framework for Multi-Scenario 🔭

"MCM adopts a two-branch architecture consisting of a main branch and a control branch, both are Transformer-based diffusion model MWNet (DDPM-like) that can capture the spatial complexity and inter-joint correlations in motion sequences through a channel-dimension self-attention module." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03031v1 #arxiv

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📝 SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution 🔭

"By clustering the extensive degradation space to create a set of representative degradation cases, which serves as a comprehensive test set; By proposing a coarse-to-fine evaluation protocol to measure the distributed and relative performance of real-SR methods on the test set." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.03020v1 #arxiv

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📝 Continual Evidential Deep Learning for Out-of-Distribution Detection 🔭

"Continual Evidential Deep Learning, which is an evidential deep learning method in a continual learning paradigm, has been evaluated on the CIFAR-100 dataset." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.02995v1 #arxiv

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📝 FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching 🔭

"Combines object detection techniques with the IoU matching algorithm, thereby achieving efficient, precise, and robust fish detection and tracking, and the algorithm employed in this method addresses the issue of missed detections without relying on complex feature matching or graph optimization algorithms." [gal30b+] 🤖 #CV

⚙ https://github.com/gakkistar/FishMOT
🔗 https://arxiv.org/abs/2309.02975v1 #arxiv

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📝 Towards Efficient Training with Negative Samples in Visual Tracking 🔭

"JN-256 introduces a distribution-based head which modeling the bounding box as distribution of distances to express uncertainty about the target's location in the presence of negative samples." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.02903v1 #arxiv

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📝 Image Aesthetics Assessment via Learnable Queries 🔭

"IAA-LQ utilizes learnable queries, which are initialized by content-related knowledge, to extract aesthetic features by attentively selecting content-related image features." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.02861v1 #arxiv

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📝 Bandwidth-Efficient Inference for Neural Image Compression 🔭

"An illustration of proposed method: (a) An end-to-end differentiable bandwidth efficient neural inference method with the activation compressed by neural data compression method, (b) An illustration of transform-quantization-entropy coding pipeline for activation compression." [gal30b+] 🤖 #CV

⚙ https://github.com/rygorous/ryg
🔗 https://arxiv.org/abs/2309.02855v1 #arxiv

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📝 Knowledge Distillation Layer That Lets the Student Decide 🔭

"Proposes a learnable knowledge distillation (letKD) layer for the student which improves KD with two distinct abilities: i) learning how to leverage the teacher's knowledge, enabling to discard nuisance information, and ii) feeding forward the transferred knowledge deeper." [gal30b+] 🤖 #CV

⚙ https://github.com/adagorgun/letKD-framework
🔗 https://arxiv.org/abs/2309.02843v1 #arxiv

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📝 Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning 🔭

"Proposes a novel FSCIL training framework, IOSFSCIL, based on CLIP's generalizability and image-object-specific (IOS) classifiers." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.02833v1 #arxiv

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📝 Diffusion Model Is Secretly a Training-Free Open Vocabulary Semantic Segmenter 🔭

"DiffSegmenter utilizes an off-the-shelf pre-trained text-conditioned diffusion model to generate cross-attention and self-attention maps, which are further refined and completed for final predictions." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.02773v1 #arxiv

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📝 DMKD: Improving Feature-Based Knowledge Distillation for Object Detection via Dual Masking Augmentation 🔭

"Develops a Dual Masked Knowledge Distillation (DMKD) framework for compressing the backbone of two-stage detector (RetinaNet and Cascade Mask R-CNN)." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.02719v1 #arxiv

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📝 Efficient Training for Visual Tracking with Deformable Transformer 🔭

"Proposes a novel end-to-end visual tracking framework, DETRack, which achieves efficient training and low inference complexity with an encoder-decoder structure, one-to-many label assignment, and auxiliary denoising techniques." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.02676v1 #arxiv

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📝 Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study 🔭

"EMO is proposed to speed up the real time object tracking on the edge by leveraging both the window-based optimization and the similarity-based optimization, to achieve a better balance of speed and accuracy." [gal30b+] 🤖 #CV #DC

⚙ https://github.com/git-disl/EMO
🔗 https://arxiv.org/abs/2309.02666v1 #arxiv

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📝 Self-Supervised Video Transformers for Isolated Sign Language Recognition 🔭

"Self-Supervised Learning, Transformers, Isolated Sign Language Recognition, Phonological Features, Sign Language Representation Learning I would like to express my sincere gratitude to Drs." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2309.02450v1 #arxiv

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📝 Classification Committee for Active Deep Object Detection 🔭

"A classification committee for active deep object detection method is proposed by introducing a discrepancy mechanism of multiple classifiers to select the most informative images, which is expected to focus more on discrepancy and representative of instances." [gal30b+] 🤖 #CV

🔗 https://arxiv.org/abs/2308.08476v1 #arxiv

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📝 Fast Adaptation with Bradley-Terry Preference Models in Text-to-Image Classification and Generation 🔭🧠

"Leverages the Bradley-Terry preference model to develop a fast preference-aware adaptation method, using few samples and with minimal computing resources, which efficiently fine-tunes the original model and aligns it with the preferences of the user." [gal30b+] 🤖 #CV #LG

🔗 https://arxiv.org/abs/2308.07929v1 #arxiv

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