12 papers
0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation
Xiaoqiang Lu, Licheng Jiao, Lingling Li +5
Recent advances in vision foundation models (VFMs) have shown remarkable capabilities across diverse unimodal visual tasks. However, adapting VFMs to referring image segmentation (…
SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation
Linnan Zhao, Xu Liu, Lingling Li +3
Reasoning segmentation converts an implicit linguistic conclusion into a precise mask, requiring both semantic identification and spatial grounding. Existing MLLM-segmenter interfa…
The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering
Yuqian Fu, Tianwen Qian, Yanjun Li +30
EgoCross is a cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scena…
ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction
Zichun Guo, Yuling Shi, Wenhao Zeng +6
Multimodal Large Language Models (MLLMs) have achieved remarkable performance in Visually Rich Document Understanding (VRDU) tasks, but their capabilities are mainly evaluated on p…
Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models
Ruiyang Li, Fang Liu, Licheng Jiao +8
Medical image segmentation supports clinical workflows by precisely delineating anatomical structures and lesions. However, medical image datasets medical image datasets suffer fro…
SoccerNet 2025 Challenges Results
Silvio Giancola, Anthony Cioppa, Marc Gutiérrez-Pérez +115
The SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in football video understandi…
Domain-aware Category-level Geometry Learning Segmentation for 3D Point Clouds
Pei He, Lingling Li, Licheng Jiao +5
Domain generalization in 3D segmentation is a critical challenge in deploying models to unseen environments. Current methods mitigate the domain shift by augmenting the data distri…
Logits DeConfusion with CLIP for Few-Shot Learning
Shuo Li, Fang Liu, Zehua Hao +5
With its powerful visual-language alignment capability, CLIP performs well in zero-shot and few-shot learning tasks. However, we found in experiments that CLIP's logits suffer from…
MASSeg : 2nd Technical Report for 4th PVUW MOSE Track
Xuqiang Cao, Linnan Zhao, Jiaxuan Zhao +3
Complex video object segmentation continues to face significant challenges in small object recognition, occlusion handling, and dynamic scene modeling. This report presents our sol…
Fast and Efficient: Mask Neural Fields for 3D Scene Segmentation
Zihan Gao, Lingling Li, Licheng Jiao +5
Understanding 3D scenes is a crucial challenge in computer vision research with applications spanning multiple domains. Recent advancements in distilling 2D vision-language foundat…
Edit-Your-Motion: Space-Time Diffusion Decoupling Learning for Video Motion Editing
Yi Zuo, Lingling Li, Licheng Jiao +5
Existing diffusion-based methods have achieved impressive results in human motion editing. However, these methods often exhibit significant ghosting and body distortion in unseen i…
Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds
Yanbiao Ma, Licheng Jiao, Fang Liu +5
Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence. Delving into deeper factors that affect the fairness of DNNs is p…