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VisualRouter: Query-Grounded Visual Sampling for Long Video Understanding
Haiyue Zhang, Yi Bin, Xun Jiang +5
Large vision-language models (LVLMs) have achieved significant progress in video understanding, yet understanding long videos remains challenging due to the large number of visual…
Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception
Mingfeng Zha, Tianyu Li, Guoqing Wang +5
Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes. Existing methods, constrained…
M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Model
Yihang Liu, Longzhen Yang, Jiaxiong Yang +3
Medical foundation models (MFMs) aim to learn universal representations from multimodal medical images that can generalize effectively to diverse downstream clinical tasks. However…
OVS-DINO: Open-Vocabulary Segmentation via Structure-Aligned SAM-DINO with Language Guidance
Haoxi Zeng, Qiankun Liu, Yi Bin +5
Open-Vocabulary Segmentation (OVS) aims to segment image regions beyond predefined category sets by leveraging semantic descriptions. While CLIP based approaches excel in semantic…
ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models
Wencheng Ye, Tianshi Wang, Lei Zhu +3
Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remains limited by heavy computational…
Self-Supervised Anatomical Consistency Learning for Vision-Grounded Medical Report Generation
Longzhen Yang, Zhangkai Ni, Ying Wen +3
Vision-grounded medical report generation aims to produce clinically accurate descriptions of medical images, anchored in explicit visual evidence to improve interpretability and f…
Unified modality separation: A vision-language framework for unsupervised domain adaptation
Xinyao Li, Jingjing Li, Zhekai Du +2
Unsupervised domain adaptation (UDA) enables models trained on a labeled source domain to handle new unlabeled domains. Recently, pre-trained vision-language models (VLMs) have dem…
Implicit Counterfactual Learning for Audio-Visual Segmentation
Mingfeng Zha, Tianyu Li, Guoqing Wang +4
Audio-visual segmentation (AVS) aims to segment objects in videos based on audio cues. Existing AVS methods are primarily designed to enhance interaction efficiency but pay limited…
DiffLoRA: Generating Personalized Low-Rank Adaptation Weights with Diffusion
Yujia Wu, Yiming Shi, Jiwei Wei +3
Personalized text-to-image generation has gained significant attention for its capability to generate high-fidelity portraits of specific identities conditioned on user-defined pro…
Interpreting and Improving Attention From the Perspective of Large Kernel Convolution
Chenghao Li, Chaoning Zhang, Boheng Zeng +7
Attention mechanisms have significantly advanced visual models by capturing global context effectively. However, their reliance on large-scale datasets and substantial computationa…
From Channel Bias to Feature Redundancy: Uncovering the "Less is More" Principle in Few-Shot Learning
Ji Zhang, Xu Luo, Lianli Gao +3
Deep neural networks often fail to adapt representations to novel tasks under distribution shifts, especially when only a few examples are available. This paper identifies a core o…