8 citations · 19 across the 22 of their papers we have counts for
12 papers · 1 filter
Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Rumination
Yolo Y. Tang, Daiki Shimada, Hang Hua +4
Understanding text-rich videos requires reading small, transient textual cues that often demand repeated inspection. Yet most video QA models rely on single-pass perception over fi…
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
Yolo Y. Tang, Jing Bi, Pinxin Liu +24
Video understanding represents the most challenging frontier in computer vision, requiring models to reason about complex spatiotemporal relationships, long-term dependencies, and…
When to Think and When to Look: Uncertainty-Guided Lookback
Jing Bi, Filippos Bellos, Junjia Guo +8
Test-time thinking (that is, generating explicit intermediate reasoning chains) is known to boost performance in large language models and has recently shown strong gains for large…
High-Quality Sound Separation Across Diverse Categories via Visually-Guided Generative Modeling
Chao Huang, Susan Liang, Yapeng Tian +2
We propose DAVIS, a Diffusion-based Audio-VIsual Separation framework that solves the audio-visual sound source separation task through generative learning. Existing methods typica…
ZeroSep: Separate Anything in Audio with Zero Training
Chao Huang, Yuesheng Ma, Junxuan Huang +6
Audio source separation is fundamental for machines to understand complex acoustic environments and underpins numerous audio applications. Current supervised deep learning approach…
FreSca: Scaling in Frequency Space Enhances Diffusion Models
Chao Huang, Susan Liang, Yunlong Tang +4
Latent diffusion models (LDMs) have achieved remarkable success in a variety of image tasks, yet achieving fine-grained, disentangled control over global structures versus fine det…