Like Humans to Few-Shot Learning through Knowledge Permeation of Vision and Text
<|reference_start|>Like Humans to Few-Shot Learning through Knowledge Permeation of Vision and Text: Few-shot learning aims to generalize the recognizer from seen categories to an entirely novel scenario. With only a few support samples, several advanced methods initially introduce class names as prior knowledge for identifying novel classes. However, obstacles still impede achieving a comprehensive understanding of how to harness the mutual advantages of visual and textual knowledge. In this paper, we propose a coherent Bidirectional Knowledge Permeation strategy called BiKop, which is grounded in a human intuition: A class name description offers a general representation, whereas an image captures the specificity of individuals. BiKop primarily establishes a hierarchical joint general-specific representation through bidirectional knowledge permeation. On the other hand, considering the bias of joint representation towards the base set, we disentangle base-class-relevant semantics during training, thereby alleviating the suppression of potential novel-class-relevant information. Experiments on four challenging benchmarks demonstrate the remarkable superiority of BiKop. Our code will be publicly available.<|reference_end|>
@article{jia2024like,
    title={Like Humans to Few-Shot Learning through Knowledge Permeation of Vision
  and Text},
    author={Yuyu Jia, Qing Zhou, Wei Huang, Junyu Gao, Qi Wang},
    journal={arXiv preprint arXiv:2405.12543},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12543},
    primaryClass={cs.CV cs.AI}
}