A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets

• URL: http://arxiv.org/abs/2403.06295v1
• Date: Sun, 10 Mar 2024 19:50:03 GMT
• Title: A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets
• Authors: Thang Doan, Sima Behpour, Xin Li, Wenbin He, Liang Gou, Liu Ren
• Abstract summary: Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams. We propose Session-Specific Prompts (SSP), which 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.
• Score: 23.005760505169803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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