27 citations · 30 across the 4 of their papers we have counts for
4 papers
Language Prompt vs. Image Enhancement: Boosting Object Detection With CLIP in Hazy Environments
Jian Pang, Bingfeng Zhang, Jin Wang +3
Object detection in hazy environments is challenging because degraded objects are nearly invisible and their semantics are weakened by environmental noise, making it difficult for…
Unbiased Semantic Decoding with Vision Foundation Models for Few-shot Segmentation
Jin Wang, Bingfeng Zhang, Jian Pang +3
Few-shot segmentation has garnered significant attention. Many recent approaches attempt to introduce the Segment Anything Model (SAM) to handle this task. With the strong generali…
Selecting task with optimal transport self-supervised learning for few-shot classification
Renjie Xu, Xinghao Yang, Baodi Liu +2
Few-Shot classification aims at solving problems that only a few samples are available in the training process. Due to the lack of samples, researchers generally employ a set of tr…
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning
Rui Xu, Lei Xing, Shuai Shao +4
Few-shot learning (FSL), purposing to resolve the problem of data-scarce, has attracted considerable attention in recent years. A popular FSL framework contains two phases: (i) the…