95 citations · 140 across the 23 of their papers we have counts for
23 papers
Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding
Tianyue Wang, Xuying Wu, Yuxiang Ma +7
Long-form video understanding remains challenging for video agents due to the mismatch between query demands and evidence acquisition strategies. Although recent planning-before-pe…
On-Policy Self-Distillation in Diffusion Models
Wei Zhou, Xiongwei Zhu, Lingdong Kong +14
Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an intermediate denoising prediction…
Optimizing Visual Generative Models via Distribution-wise Rewards
Ruihang Li, Mengde Xu, Shuyang Gu +4
Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results in reward hacking that degr…
DanceOPD: On-Policy Generative Field Distillation
Wei Zhou, Xiongwei Zhu, Zelin Xu +8
Modern image generation demands a single model that unifies diverse capabilities, including text-to-image (T2I), local editing, and global editing. However, these capabilities are…
OSGNet with MLLM Reranking @ Ego4D Episodic Memory Challenge 2026
Yisen Feng, Leigang Qu, Haoyu Zhang +5
In this report, we present our champion solutions for the Natural Language Queries and GoalStep tracks of the Ego4D Episodic Memory Challenge at CVPR 2026. Both tracks require accu…
AI for Auto-Research: Roadmap & User Guide
Lingdong Kong, Xian Sun, Wei Chow +17
AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draf…
ReCALL: Recalibrating Capability Degradation for MLLM-based Composed Image Retrieval
Tianyu Yang, Chenwei He, Xiangzhao Hao +6
Composed Image Retrieval (CIR) aims to retrieve target images based on a hybrid query comprising a reference image and a modification text. Early dual-tower Vision-Language Models…
WISER: Wider Search, Deeper Thinking, and Adaptive Fusion for Training-Free Zero-Shot Composed Image Retrieval
Tianyue Wang, Leigang Qu, Tianyu Yang +4
Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images given a multimodal query (comprising a reference image and a modification text), without training on anno…
GenArena: How Can We Achieve Human-Aligned Evaluation for Visual Generation Tasks?
Ruihang Li, Leigang Qu, Jingxu Zhang +6
The rapid advancement of visual generation models has outpaced traditional evaluation approaches, necessitating the adoption of Vision-Language Models as surrogate judges. In this…
AUHead: Realistic Emotional Talking Head Generation via Action Units Control
Jiayi Lyu, Leigang Qu, Wenjing Zhang +6
Realistic talking-head video generation is critical for virtual avatars, film production, and interactive systems. Current methods struggle with nuanced emotional expressions due t…
TTOM: Test-Time Optimization and Memorization for Compositional Video Generation
Leigang Qu, Ziyang Wang, Na Zheng +3
Video Foundation Models (VFMs) exhibit remarkable visual generation performance, but struggle in compositional scenarios (e.g., motion, numeracy, and spatial relation). In this wor…
VINCIE: Unlocking In-context Image Editing from Video
Leigang Qu, Feng Cheng, Ziyan Yang +7
In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pi…
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos
Haowen Gao, Liang Pang, Shicheng Xu +4
With the rapid development of AI-generated content (AIGC), the creation of high-quality AI-generated videos has become faster and easier, resulting in the Internet being flooded wi…
SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation
Leigang Qu, Haochuan Li, Wenjie Wang +4
Large Multimodal Models (LMMs) have demonstrated impressive capabilities in multimodal understanding and generation, pushing forward advancements in text-to-image generation. Howev…
Revolutionizing Text-to-Image Retrieval as Autoregressive Token-to-Voken Generation
Yongqi Li, Hongru Cai, Wenjie Wang +5
Text-to-image retrieval is a fundamental task in multimedia processing, aiming to retrieve semantically relevant cross-modal content. Traditional studies have typically approached…
TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models
Leigang Qu, Haochuan Li, Tan Wang +4
How humans can effectively and efficiently acquire images has always been a perennial question. A classic solution is text-to-image retrieval from an existing database; however, th…
Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives
Thong Nguyen, Yi Bin, Junbin Xiao +6
Humans use multiple senses to comprehend the environment. Vision and language are two of the most vital senses since they allow us to easily communicate our thoughts and perceive t…
Discriminative Probing and Tuning for Text-to-Image Generation
Leigang Qu, Wenjie Wang, Yongqi Li +3
Despite advancements in text-to-image generation (T2I), prior methods often face text-image misalignment problems such as relation confusion in generated images. Existing solutions…
Generative Cross-Modal Retrieval: Memorizing Images in Multimodal Language Models for Retrieval and Beyond
Yongqi Li, Wenjie Wang, Leigang Qu +3
The recent advancements in generative language models have demonstrated their ability to memorize knowledge from documents and recall knowledge to respond to user queries effective…
NExT-GPT: Any-to-Any Multimodal LLM
Shengqiong Wu, Hao Fei, Leigang Qu +2
While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without t…
LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation
Leigang Qu, Shengqiong Wu, Hao Fei +2
In the text-to-image generation field, recent remarkable progress in Stable Diffusion makes it possible to generate rich kinds of novel photorealistic images. However, current mode…
Learnable Pillar-based Re-ranking for Image-Text Retrieval
Leigang Qu, Meng Liu, Wenjie Wang +3
Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities. Prior work usually focuses on the pairwise relations (i.e., wh…
Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization
Yiyang Chen, Zhedong Zheng, Wei Ji +2
We investigate composed image retrieval with text feedback. Users gradually look for the target of interest by moving from coarse to fine-grained feedback. However, existing method…