activity
20242026
most citedCustomize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts

3 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

PRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing

Chengyu Fang, Chunming He, Yuelin Zhang +6

Real-world image dehazing (RID) aims to remove haze-induced degradation from real scenes. This task remains challenging due to non-uniform haze distribution, spatially varying colo…

cs.GR2025★ 1 cited

Controllable Video Generation: A Survey

Yue Ma, Kunyu Feng, Zhongyuan Hu +19

With the rapid development of AI-generated content (AIGC), video generation has emerged as one of its most dynamic and impactful subfields. In particular, the advancement of video…

cs.CV2025

AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era

Chenyang Zhu, Xing Zhang, Yuyang Sun +2

Recent advances in image generation, particularly diffusion models, have significantly lowered the barrier for creating sophisticated forgeries, making image manipulation detection…

cs.CV2025★ 1 cited

Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well

Chengyu Fang, Chunming He, Longxiang Tang +6

Camouflaged Object Segmentation (COS) remains challenging because camouflaged objects exhibit only subtle visual differences from their backgrounds and single-modality RGB methods…

cs.CV2024★ 3 cited

Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts

Chenyang Zhu, Bin Xiao, Lin Shi +2

The recent Segment Anything Model (SAM) represents a significant breakthrough in scaling segmentation models, delivering strong performance across various downstream applications i…

cs.CV2024

InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences

Chenyang Zhu, Kai Li, Yue Ma +5

Recent advances in Customized Concept Swapping (CCS) enable a text-to-image model to swap a concept in the source image with a customized target concept. However, the existing meth…

cs.CV2024★ 1 cited

MultiBooth: Towards Generating All Your Concepts in an Image from Text

Chenyang Zhu, Kai Li, Yue Ma +2

This paper introduces MultiBooth, a novel and efficient technique for multi-concept customization in image generation from text. Despite the significant advancements in customized…