6 papers
Prototype-Guided Curriculum Learning for Zero-Shot Learning
Lei Wang, Shiming Chen, Guo-Sen Xie +4
In Zero-Shot Learning (ZSL), embedding-based methods enable knowledge transfer from seen to unseen classes by learning a visual-semantic mapping from seen-class images to class-lev…
Few-Shot Object Detection via Spatial-Channel State Space Model
Zhimeng Xin, Tianxu Wu, Yixiong Zou +3
Due to the limited training samples in few-shot object detection (FSOD), we observe that current methods may struggle to accurately extract effective features from each channel. Sp…
Toward Realistic Camouflaged Object Detection: Benchmarks and Method
Zhimeng Xin, Tianxu Wu, Shiming Chen +5
Camouflaged object detection (COD) primarily relies on semantic or instance segmentation methods. While these methods have made significant advancements in identifying the contours…
Discriminative Image Generation with Diffusion Models for Zero-Shot Learning
Dingjie Fu, Wenjin Hou, Shiming Chen +4
Generative Zero-Shot Learning (ZSL) methods synthesize class-related features based on predefined class semantic prototypes, showcasing superior performance. However, this feature…
Visual-Semantic Graph Matching Net for Zero-Shot Learning
Bowen Duan, Shiming Chen, Yufei Guo +3
Zero-shot learning (ZSL) aims to leverage additional semantic information to recognize unseen classes. To transfer knowledge from seen to unseen classes, most ZSL methods often lea…
Concept Drift and Long-Tailed Distribution in Fine-Grained Visual Categorization: Benchmark and Method
Shuo Ye, Shiming Chen, Ruxin Wang +5
Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categori…