4 accepted papers
Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual samples. Existing methods attempt to incorporate semantic information into the limited visual data for category understanding. However, these methods often enrich class-level feature representations with abstract…
Existing adaptation methods of pre-trained vision-language models like CLIP often rely on base-class samples during fine-tuning, introducing systematic biases that distort decision boundaries and degrade performance on novel classes. In this work, we break new ground by proposing a hierarchical divi…
"The excellent generalization capability of pre-trained Vision-Language Models (VLMs) makes fine-tuning VLMs for downstream zero-shot tasks a popular choice. Despite achieving promising performance in the professionality of base classes, most existing fine-tuned methods suffer from feature confusion…
"Existing methods attempt to improve models’ generalization ability on real-world hazy images by exploring well-designed training schemes (, CycleGAN, prior loss). However, most of them need very complicated training procedures to achieve satisfactory results. For the first time, we present a novel…