5 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
Motion-Guided Semantic Alignment with Negative Prompts for Zero-Shot Video Action Recognition
Yiming Wang, Frederick W. B. Li, Jingyun Wang
Zero-shot action recognition is challenging due to the semantic gap between seen and unseen classes. We present a novel framework that enhances CLIP with disentangled embeddings an…
CrossVid: A Comprehensive Benchmark for Evaluating Cross-Video Reasoning in Multimodal Large Language Models
Jingyao Li, Jingyun Wang, Molin Tan +6
Cross-Video Reasoning (CVR) presents a significant challenge in video understanding, which requires simultaneous understanding of multiple videos to aggregate and compare informati…
LTCA: Long-range Temporal Context Attention for Referring Video Object Segmentation
Cilin Yan, Jingyun Wang, Guoliang Kang
Referring Video Segmentation (RVOS) aims to segment objects in videos given linguistic expressions. The key to solving RVOS is to extract long-range temporal context information fr…
Separate Motion from Appearance: Customizing Motion via Customizing Text-to-Video Diffusion Models
Huijie Liu, Jingyun Wang, Shuai Ma +3
Motion customization aims to adapt the diffusion model (DM) to generate videos with the motion specified by a set of video clips with the same motion concept. To realize this goal,…
ReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation
Jingyun Wang, Guoliang Kang
Recent works utilize CLIP to perform the challenging unsupervised semantic segmentation task where only images without annotations are available. However, we observe that when adop…