7 papers
Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement
Guangqian Guo, Aixi Ren, Yong Guo +6
Segment Anything Models (SAMs), known for their exceptional zero-shot segmentation performance, have garnered significant attention in the research community. Nevertheless, their p…
Segment Any-Quality Images with Generative Latent Space Enhancement
Guangqian Guo, Yong Guo, Xuehui Yu +3
Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting their effectiveness in real-world…
Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios
Guangqian Guo, Pengfei Chen, Yong Guo +3
Segment Anything Model (SAM), known for its remarkable zero-shot segmentation capabilities, has garnered significant attention in the community. Nevertheless, its performance is ch…
ReLayout: Integrating Relation Reasoning for Content-aware Layout Generation with Multi-modal Large Language Models
Jiaxu Tian, Xuehui Yu, Yaoxing Wang +3
Content-aware layout aims to arrange design elements appropriately on a given canvas to convey information effectively. Recently, the trend for this task has been to leverage large…
Why mamba is effective? Exploit Linear Transformer-Mamba Network for Multi-Modality Image Fusion
Chenguang Zhu, Shan Gao, Huafeng Chen +5
Multi-modality image fusion aims to integrate the merits of images from different sources and render high-quality fusion images. However, existing feature extraction and fusion met…
Just a Hint: Point-Supervised Camouflaged Object Detection
Huafeng Chen, Dian Shao, Guangqian Guo +1
Camouflaged Object Detection (COD) demands models to expeditiously and accurately distinguish objects which conceal themselves seamlessly in the environment. Owing to the subtle di…