activity
20242026
most citedUncover and Unlearn Nuisances: Agnostic Fully Test-Time Adaptation

1 citations · 1 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2026

CameraEditor: Camera-Controlled Image Editing via Video-Prior Sequential Modeling

Xin Shen, Chengyou Jia, Keshuo Xing +6

Beyond semantic content, camera parameters play a pivotal role in dictating the geometric perspective and appearance of any given image. While recent image editing models excel at…

cs.CV2026

Knowledge is Power: Advancing Few-shot Action Recognition with Multimodal Semantics from MLLMs

Jiazheng Xing, Chao Xu, Hangjie Yuan +4

Multimodal Large Language Models (MLLMs) have propelled the field of few-shot action recognition (FSAR). However, preliminary explorations in this area primarily focus on generatin…

cs.LG2026

Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models

Bowen Ping, Chengyou Jia, Minnan Luo +2

Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow-matching models with human preferences, yet practitioners face fragmented codebases, mode…

cs.CV2025

PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling

Bowen Ping, Chengyou Jia, Minnan Luo +4

Consistent image generation requires faithfully preserving identities, styles, and logical coherence across multiple images, which is essential for applications such as storytellin…

cs.LG2025★ 1 cited

Uncover and Unlearn Nuisances: Agnostic Fully Test-Time Adaptation

Ponhvoan Srey, Yaxin Shi, Hangwei Qian +2

Fully Test-Time Adaptation (FTTA) addresses domain shifts without access to source data and training protocols of the pre-trained models. Traditional strategies that align source a…

cs.CV2025

Multi-Modal Dataset Distillation in the Wild

Zhuohang Dang, Minnan Luo, Chengyou Jia +3

Recent multi-modal models have shown remarkable versatility in real-world applications. However, their rapid development encounters two critical data challenges. First, the trainin…

cs.CV2025

Why Settle for One? Text-to-ImageSet Generation and Evaluation

Chengyou Jia, Xin Shen, Zhuohang Dang +6

Despite remarkable progress in Text-to-Image models, many real-world applications require generating coherent image sets with diverse consistency requirements. Existing consistent…

cs.CV2024

ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting

Chengyou Jia, Changliang Xia, Zhuohang Dang +3

Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the c…