1 citations · 4 across the 28 of their papers we have counts for
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Evidence-RL: Towards Evidence-intensive Visual Reasoning
Haojie Huang, Xinlei Yu, Chengming Xu +6
Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context. Existing perception-aware pos…
SPOT-E: Test-Time Entropy Shaping with Visual Spotlights for Frozen VLMs
Bo Yin, Xiaobin Hu, Chengming Xu +6
Vision-language models (VLMs) often underperform on evidence intensive tasks because decisive visual evidence are small, localized, and easy to overlook, leading to failures in evi…
What Semantics Survive the Connector? Diagnosing VLM-to-DiT Alignment in Video Editing
Hangyu Lin, Chao Wen, Chengming Xu +4
Flow matching based video generative models have been increasingly relying on prepended Vision-Language Models (VLMs) to handle complex, instruction-based video editing. The prevai…
PixVerve: Advancing Native UHR Image Generation to 100MP with a Large-Scale High-Quality Dataset
Haojun Chen, Haoyang He, Chengming Xu +11
Text-to-Image (T2I) models have recently seen notable progress around 1K and 2K resolution. With the extreme desire for better visual experience and the rapid development of imagin…
The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
Xinlei Yu, Zhangquan Chen, Yongbo He +36
Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an inc…
Dual Latent Memory for Visual Multi-agent System
Xinlei Yu, Chengming Xu, Zhangquan Chen +8
While Visual Multi-Agent Systems (VMAS) promise to enhance comprehensive abilities through inter-agent collaboration, empirical evidence reveals a counter-intuitive "scaling wall":…
FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing
Xijie Huang, Chengming Xu, Donghao Luo +6
First-Frame Propagation (FFP) offers a promising paradigm for controllable video editing, but existing methods are hampered by a reliance on cumbersome run-time guidance. We identi…