42 citations · 60 across the 13 of their papers we have counts for
6 papers · 1 filter
Universal Object Detection with Large Vision Model
Feng Lin, Wenze Hu, Yaowei Wang +5
Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system. Such systems have the potential to address a…
Semantic-Aware Local-Global Vision Transformer
Jiatong Zhang, Zengwei Yao, Fanglin Chen +2
Vision Transformers have achieved remarkable progresses, among which Swin Transformer has demonstrated the tremendous potential of Transformer for vision tasks. It surmounts the ke…
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution
Fengjun Li, Xin Feng, Fanglin Chen +2
Typical methods for blind image super-resolution (SR) focus on dealing with unknown degradations by directly estimating them or learning the degradation representations in a latent…
Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations
Wenjie Pei, Shuang Wu, Dianwen Mei +3
While fine-tuning based methods for few-shot object detection have achieved remarkable progress, a crucial challenge that has not been addressed well is the potential class-specifi…
Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection
Shuang Wu, Wenjie Pei, Dianwen Mei +3
Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and…
Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration
Xin Feng, Haobo Ji, Wenjie Pei +2
While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e.g., 4K) images remains…