2 citations · 3 across the 10 of their papers we have counts for
13 papers
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…
Massive Activations are the Key to Local Detail Synthesis in Diffusion Transformers
Chaofan Gan, Zicheng Zhao, Yuanpeng Tu +5
Diffusion Transformers (DiTs) have recently emerged as a powerful backbone for visual generation. Recent observations reveal \emph{Massive Activations} (MAs) in their internal feat…
Enhancing Video Large Language Models with Structured Multi-Video Collaborative Reasoning
Zhihao He, Tianyao He, Yun Xu +5
Despite the prosperity of the video language model, the current pursuit of comprehensive video reasoning is thwarted by the inherent spatio-temporal incompleteness within individua…
MCA: 2D-3D Retrieval with Noisy Labels via Multi-level Adaptive Correction and Alignment
Gui Zou, Chaofan Gan, Chern Hong Lim +2
With the increasing availability of 2D and 3D data, significant advancements have been made in the field of cross-modal retrieval. Nevertheless, the existence of imperfect annotati…
Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue Reasoning
Tieyuan Chen, Huabin Liu, Yi Wang +8
Video Question Answering (VideoQA) aims to answer natural language questions based on the given video, with prior work primarily focusing on identifying the duration of relevant se…
Unleashing Diffusion Transformers for Visual Correspondence by Modulating Massive Activations
Chaofan Gan, Yuanpeng Tu, Xi Chen +4
Pre-trained stable diffusion models (SD) have shown great advances in visual correspondence. In this paper, we investigate the capabilities of Diffusion Transformers (DiTs) for acc…
MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning
Tieyuan Chen, Huabin Liu, Yi Wang +5
Video causal reasoning aims to achieve a high-level understanding of videos from a causal perspective. However, it exhibits limitations in its scope, primarily executed in a questi…
MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning
Tieyuan Chen, Huabin Liu, Tianyao He +8
Video causal reasoning aims to achieve a high-level understanding of video content from a causal perspective. However, current video reasoning tasks are limited in scope, primarily…
DAC: 2D-3D Retrieval with Noisy Labels via Divide-and-Conquer Alignment and Correction
Chaofan Gan, Yuanpeng Tu, Yuxi Li +1
With the recent burst of 2D and 3D data, cross-modal retrieval has attracted increasing attention recently. However, manual labeling by non-experts will inevitably introduce corrup…