activity
20202025
most citedTrustworthy Representation Learning Across Domains

2 citations · 2 across the 8 of their papers we have counts for

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Showing cs.CVShow all

6 papers · 1 filter

cs.CV2025

HFS: Holistic Query-Aware Frame Selection for Efficient Video Understanding

Yiqing Yang, Yun Li, Daiqing Qi +5

Key frame selection is essentially a set-level optimization problem: the quality of the selected subset depends on the interactions among frames, rather than the score of any singl…

cs.CV2025

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…

cs.CV2025

The Photographer Eye: Teaching Multimodal Large Language Models to Understand Image Aesthetics like Photographers

Daiqing Qi, Handong Zhao, Jing Shi +5

While editing directly from life, photographers have found it too difficult to see simultaneously both the blue and the sky. Photographer and curator, Szarkowski insightfully revea…

cs.CV2025

ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL

Yu Zhang, Yunqi Li, Yifan Yang +7

Although chain-of-thought reasoning and reinforcement learning (RL) have driven breakthroughs in NLP, their integration into generative vision models remains underexplored. We intr…

cs.CV2025

VRMDiff: Text-Guided Video Referring Matting Generation of Diffusion

Lehan Yang, Jincen Song, Tianlong Wang +4

We propose a new task, video referring matting, which obtains the alpha matte of a specified instance by inputting a referring caption. We treat the dense prediction task of mattin…

cs.CV2024

Reminding Multimodal Large Language Models of Object-aware Knowledge with Retrieved Tags

Daiqing Qi, Handong Zhao, Zijun Wei +1

Despite recent advances in the general visual instruction-following ability of Multimodal Large Language Models (MLLMs), they still struggle with critical problems when required to…