2 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
VGGA: Geometry-Grounded Autoencoder for Video Generation
Chaofan Gan, Yian Zhao, Shijie Li +4
Autoencoders are the core component of latent video generative models, compressing high-dimensional videos into compact latent spaces for downstream generation. However, existing v…
Awakening Diffusion Transformers: Eliciting Stronger Generation and Understanding via Massive Activation Modulation
Chaofan Gan, Zicheng Zhao, Yuanpeng Tu +6
Massive Activations (MAs) have been widely observed in Transformer-based models, yet their structure and functional roles in Diffusion Transformers (DiTs) remain insufficiently und…
Multimodal Continuous Reasoning via Asymmetric Mutual Variational Learning
Shijie Li, Yilin Gao, Siyuan Yang +7
Multimodal Large Language Models (MLLMs) are often constrained by a language-space bottleneck, forcing complex visual reasoning into discrete tokens which can lose perceptual nuanc…
From Priors to Perception: Grounding Video-LLMs in Physical Reality
Zicheng Zhao, Chaofan Gan, Shijie Li +1
Video Large Language Models (Video-LLMs) excel in general video understanding but often base physical judgments on event expectations rather than observations. We find that they no…
VidLaDA: Bidirectional Diffusion Large Language Models for Efficient Video Understanding
Zhihao He, Tieyuan Chen, Kangyu Wang +6
Current Video Large Language Models (Video LLMs) typically encode frames via a vision encoder and employ an autoregressive (AR) LLM for understanding and generation. However, this…