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

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

collaborators

5 papers

cs.GR2025

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

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.CV20243 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…