5 citations · 10 across the 10 of their papers we have counts for
6 papers · 1 filter
From Inpainting to Layer Decomposition: Repurposing Generative Inpainting Models for Image Layer Decomposition
Jingxi Chen, Yixiao Zhang, Xiaoye Qian +4
Images can be viewed as layered compositions, foreground objects over background, with potential occlusions. This layered representation enables independent editing of elements, of…
MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models
Xiyang Wu, Zongxia Li, Jihui Jin +7
Vision Language Models (VLMs) perform well on standard video tasks but struggle with physics-related reasoning involving motion dynamics and spatial interactions. We present a nove…
First Frame Is the Place to Go for Video Content Customization
Jingxi Chen, Zongxia Li, Zhichao Liu +6
What role does the first frame play in video generation models? Traditionally, it's viewed as the spatial-temporal starting point of a video, merely a seed for subsequent animation…
Semantically-Aware Rewards for Open-Ended R1 Training in Free-Form Generation
Zongxia Li, Yapei Chang, Yuhang Zhou +4
Evaluating open-ended long-form generation is challenging because it is hard to define what clearly separates good from bad outputs. Existing methods often miss key aspects like co…
VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
Zongxia Li, Xiyang Wu, Guangyao Shi +6
Vision-Language Models (VLMs) have achieved strong results in video understanding, yet a key question remains: do they truly comprehend visual content or only learn shallow correla…
A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges
Zongxia Li, Xiyang Wu, Hongyang Du +3
Large vision-language models (VLMs) have evolved rapidly from contrastive image-text encoders and adapter-based assistants into natively multimodal foundation models that support l…