5 citations · 7 across the 5 of their papers we have counts for
6 papers
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…
Few-Shot Object Detection: Research Advances and Challenges
Zhimeng Xin, Shiming Chen, Tianxu Wu +3
Object detection as a subfield within computer vision has achieved remarkable progress, which aims to accurately identify and locate a specific object from images or videos. Such m…
ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection
Zhimeng Xin, Tianxu Wu, Shiming Chen +3
Few-shot object detection (FSOD) identifies objects from extremely few annotated samples. Most existing FSOD methods, recently, apply the two-stage learning paradigm, which transfe…
Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions
Tianxu Wu, Shuo Ye, Shuhuang Chen +2
The challenge in fine-grained visual categorization lies in how to explore the subtle differences between different subclasses and achieve accurate discrimination. Previous researc…
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…