Causal Disentanglement and Cross-Modal Alignment for Enhanced Few-Shot Learning
This work addresses few-shot learning challenges for AI systems requiring adaptation with limited data, representing a novel method rather than an incremental improvement.
The paper tackles the problem of few-shot learning by proposing Causal CLIP Adapter (CCA), which explicitly disentangles visual features from CLIP using unsupervised ICA and enhances cross-modal alignment, achieving state-of-the-art performance on 11 benchmark datasets with improved robustness and computational efficiency.
Few-shot learning (FSL) often requires effective adaptation of models using limited labeled data. However, most existing FSL methods rely on entangled representations, requiring the model to implicitly recover the unmixing process to obtain disentangled representations using only limited supervision, which hinders effective adaptation. Recent theoretical studies show that multimodal contrastive learning methods, such as CLIP, can disentangle latent representations up to linear transformations. In light of this, we propose the Causal CLIP Adapter (CCA), a novel framework that explicitly disentangles visual features extracted from CLIP using unsupervised Independent Component Analysis (ICA). This removes the need to learn the unmixing process from the labeled data, thereby reducing the number of trainable parameters and mitigating overfitting. Taking a step further, while ICA can obtain visual disentangled representations, it may also disrupt CLIP's intra- and inter-modal alignment. To counteract this, CCA further leverages CLIP's inherent cross-modal alignment by enhancing it in two ways: unidirectionally, through fine-tuning a CLIP-based text classifier, and bidirectionally, via a cross-attention mechanism that enriches visual and textual representations through mutual interaction. Both unimodal and cross-modal classification outputs can be effectively combined linearly to improve classification accuracy. Extensive experiments on 11 benchmark datasets demonstrate that our method consistently outperforms state-of-the-art approaches in terms of few-shot performance and robustness to distributional shifts, while maintaining computational efficiency. Code will be available at https://github.com/tianjiao-j/CCA.