While the CMD framework demonstrates significant improvements in speech-image retrieval, it does have potential limitations. One major limitation is its reliance on the quality of the pre-trained models (HuBERT and CLIP) used for feature extraction. If these models do not adequately capture the nuances of the modalities involved, the performance of the CMD framework may be compromised.
Additionally, the current approach primarily focuses on aligning global features, which may overlook intricate local relationships within the modalities. This could lead to challenges in scenarios where fine-grained details are crucial for accurate retrieval, such as distinguishing between similar objects in images or understanding context in speech.
To address these limitations, future improvements could include the integration of more advanced feature extraction techniques that capture richer representations of the modalities. For example, employing hierarchical models that analyze both global and local features could enhance the framework's ability to understand complex cross-modal relationships.
Furthermore, incorporating attention mechanisms that dynamically focus on relevant parts of the input during the denoising process could improve the model's sensitivity to subtle differences between modalities. This would allow for a more nuanced understanding of the interactions between speech and image features, ultimately leading to better performance in complex retrieval tasks.