A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets
<|reference_start|>A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets: Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting. The rise of Vision-Language models (VLMs) has unlocked numerous applications, leveraging their existing knowledge to fine-tune on custom data. However, training the whole model is computationally prohibitive, and VLMs while being versatile in general domains still struggle with fine-grained datasets crucial for many applications. We tackle these challenges with two proposed simple modules. The first, Session-Specific Prompts (SSP), enhances the separability of image-text embeddings across sessions. The second, Hyperbolic distance, compresses representations of image-text pairs within the same class while expanding those from different classes, leading to better representations. Experimental results demonstrate an average 10-point increase compared to baselines while requiring at least 8 times fewer trainable parameters. This improvement is further underscored on our three newly introduced fine-grained datasets.<|reference_end|>
@article{doan2024a,
    title={A streamlined Approach to Multimodal Few-Shot Class Incremental Learning
  for Fine-Grained Datasets},
    author={Thang Doan, Sima Behpour, Xin Li, Wenbin He, Liang Gou, Liu Ren},
    journal={arXiv preprint arXiv:2403.06295},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06295},
    primaryClass={cs.CV}
}