2 citations · 2 across the 12 of their papers we have counts for
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DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton
Jintao Cheng, Weibin Li
Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action semantics depend on coordinated…
Test-Time Prototype Adaptation for Open-Vocabulary Semantic Segmentation
Haozhe Wang, Jintao Cheng, Weibin Li +1
Open-vocabulary semantic segmentation (OVSS) repurposes a pretrained CLIP encoder for dense prediction without additional labeled supervision. Existing methods improve CLIP's spati…
Training-Free Interaction-Aligned Visual Token Pruning for Efficient Embodied Manipulation
Jintao Cheng, Weibin Li, Haozhe Wang +7
Efficient visual representation is a central image-processing challenge in embodied manipulation, where policies repeatedly process dense visual-token sequences during closed-loop…
Beyond First-Order: Learning Riemannian Geometries for Invariant Visual Place Recognition
Jintao Cheng, Weibin Li, Zhijian He +3
Visual Place Recognition (VPR) demands representations robust to drastic environmental and viewpoint shifts. Existing aggregation paradigms either depend on extensive supervised tr…
Preserve and Sculpt: Manifold-Aligned Fine-tuning of Vision-Language Models for Few-Shot Learning
Dexia Chen, Qianjie Zhu, Weibing Li +3
Pretrained vision-language models (VLMs), such as CLIP, have shown remarkable potential in few-shot image classification and led to numerous effective transfer learning strategies.…
Cross-Domain Few-Shot Learning via Multi-View Collaborative Optimization with Vision-Language Models
Dexia Chen, Wentao Zhang, Qianjie Zhu +4
Vision-language models (VLMs) pre-trained on natural image and language data, such as CLIP, have exhibited significant potential in few-shot image recognition tasks, leading to dev…