2 citations · 2 across the 10 of their papers we have counts for
7 papers · 1 filter
Beyond Accuracy: Evaluating Grounded Visual Evidence in Thinking with Images
Xuchen Li, Xuzhao Li, Renjie Pi +3
Despite the remarkable progress of Vision-Language Models (VLMs) in adopting "Thinking-with-Images" capabilities, accurately evaluating the authenticity of their reasoning process…
MATrack: Efficient Multiscale Adaptive Tracker for Real-Time Nighttime UAV Operations
Xuzhao Li, Xuchen Li, Shiyu Hu
Nighttime UAV tracking faces significant challenges in real-world robotics operations. Low-light conditions not only limit visual perception capabilities, but cluttered backgrounds…
Select Less, Reason More: Prioritizing Evidence Purity for Video Reasoning
Xuchen Li, Xuzhao Li, Shiyu Hu +1
Long-form video reasoning remains a major challenge for Video Large Language Models (Video LLMs), as static uniform frame sampling leads to information dilution and obscures critic…
Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning
Xuchen Li, Xuzhao Li, Jiahui Gao +3
Vision-Language Models (VLMs) excel at many multimodal tasks, yet they frequently struggle with tasks requiring precise understanding and handling of fine-grained visual elements.…
CausalStep: A Benchmark for Explicit Stepwise Causal Reasoning in Videos
Xuchen Li, Xuzhao Li, Shiyu Hu +2
Recent advances in large language models (LLMs) have improved reasoning in text and image domains, yet achieving robust video reasoning remains a significant challenge. Existing vi…
DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking
Xuzhao Li, Xuchen Li, Shiyu Hu
Nighttime UAV tracking presents significant challenges due to extreme illumination variations and viewpoint changes, which severely degrade tracking performance. Existing approache…
FIOVA: A Multi-Annotator Benchmark for Human-Aligned Video Captioning
Shiyu Hu, Xuchen Li, Xuzhao Li +4
Despite rapid progress in large vision-language models (LVLMs), existing video caption benchmarks remain limited in evaluating their alignment with human understanding. Most rely o…