Long-video question answering is challenging since the answer often hinges on a few decisive moments scattered throughout the video, while memory constraints force Multimodal Large Language Models (MLLMs) to sample frames at extremely low rates, resulting in sparsity that risks omitting key evidence. Though aggressivel...
Xu-Sheng Liang, Jian-Qiao Sun, Hao Zhang et al.· 0 citations
This work proposes CLIPtrase, a novel training-free semantic segmentation strategy, which enhances local cognition through recalibrated self-correlation among patches and quadrant mask ensemble strategy, which demonstrates significant improvements in segmentation accuracy and the ability to maintain semantic coherence...
Tong Shao, Zhuo-Tao Tian, Yun-Yang Mo et al.· IEEE Transactions on Pattern...· 0 citations
PlayWorld is introduced, a benchmark providing 171 scenarios, each with a specified objective, to assess models along four core dimensions: geometry consistency, interaction fidelity, out-of-sight evolution, and insight evolution, and basic ability metrics for video quality and controllability.
Kai Ding, Xi Chen, Ming-Hong Cai et al.· 3 citations
This work proposes VLAct, a VLA-oriented VLM backbone trained on broad, heterogeneous, multi-embodiment robot data before task-specific fine-tuning, showing that representation-centric continued pre-training can deliver highly competitive performance under a modest compute budget and is an important independent axis of...
Sen-Qiao Yang, Cheng-Yao Wang, Yu-Xin Chen et al.· 8 citations
The OmniEdit-Bench provides a comprehensive and reliable testbed for evaluating instruction-based video editing and offers insights into future research directions, including an accuracy-aware penalty mechanism that conditions other scores on accuracy, preventing visually plausible but incorrect edits from receiving in...
Chenxuan Miao, Yutong Feng, Yi Lu et al.· 1 citation
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