95 citations · 140 across the 23 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…