32 citations · 55 across the 17 of their papers we have counts for
11 papers · 1 filter
Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection
Yi Liu, Chengxin Li, Xiaohui Dong +4
Achieving joint learning of Salient Object Detection (SOD) and Camouflaged Object Detection (COD) is extremely challenging due to their distinct object characteristics, i.e., salie…
LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding
Hao Li, Minghan Qin, Zhengyu Zou +6
Applying Gaussian Splatting to perception tasks for 3D scene understanding is becoming increasingly popular. Most existing works primarily focus on rendering 2D feature maps from n…
PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model
Yuqing Wang, Zhongling Huang, Shuxin Yang +4
PolSAR data presents unique challenges due to its rich and complex characteristics. Existing data representations, such as complex-valued data, polarimetric features, and amplitude…
CONDA: Condensed Deep Association Learning for Co-Salient Object Detection
Long Li, Nian Liu, Dingwen Zhang +6
Inter-image association modeling is crucial for co-salient object detection. Despite satisfactory performance, previous methods still have limitations on sufficient inter-image ass…
Mamba Capsule Routing Towards Part-Whole Relational Camouflaged Object Detection
Dingwen Zhang, Liangbo Cheng, Yi Liu +2
The part-whole relational property endowed by Capsule Networks (CapsNets) has been known successful for camouflaged object detection due to its segmentation integrity. However, the…
Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement
Xianmin Chen, Longfei Han, Peiliang Huang +3
Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge. Many deep learning-based me…
ASPS: Augmented Segment Anything Model for Polyp Segmentation
Huiqian Li, Dingwen Zhang, Jieru Yao +3
Polyp segmentation plays a pivotal role in colorectal cancer diagnosis. Recently, the emergence of the Segment Anything Model (SAM) has introduced unprecedented potential for polyp…
Center-Sensitive Kernel Optimization for Efficient On-Device Incremental Learning
Dingwen Zhang, Yan Li, De Cheng +2
To facilitate the evolution of edge intelligence in ever-changing environments, we study on-device incremental learning constrained in limited computation resource in this paper. C…
Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports
Guangyu Guo, Jiawen Yao, Yingda Xia +7
The absence of adequately sufficient expert-level tumor annotations hinders the effectiveness of supervised learning based opportunistic cancer screening on medical imaging. Clinic…
Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation
Dingwen Zhang, Hao Li, Diqi He +4
In recent times, following the paradigm of DETR (DEtection TRansformer), query-based end-to-end instance segmentation (QEIS) methods have exhibited superior performance compared to…
Revisiting the Power of Prompt for Visual Tuning
Yuzhu Wang, Lechao Cheng, Chaowei Fang +3
Visual prompt tuning (VPT) is a promising solution incorporating learnable prompt tokens to customize pre-trained models for downstream tasks. However, VPT and its variants often e…