The proposed cross-modal denoising (CMD) framework can be effectively extended to various multimodal tasks, such as video-audio retrieval, text-image alignment, and even audio-visual scene understanding. The core principle of CMD—reconstructing semantic features from noisy inputs by leveraging information from another modality—can be adapted to these tasks by following a similar denoising approach.
For instance, in video-audio retrieval, the framework could utilize video frames as one modality and corresponding audio tracks as another. By introducing noise to the audio features and reconstructing them using visual cues from the video, the model can enhance the alignment between audio and visual content, thereby improving retrieval accuracy.
In text-image alignment tasks, CMD can be employed to denoise textual embeddings by incorporating visual context, allowing the model to focus on specific image regions that correspond to the text. This could lead to better performance in applications like image captioning or visual question answering.
Moreover, the framework's ability to operate solely during training without affecting inference time makes it particularly appealing for real-time applications across different domains. By fine-tuning the CMD task for specific multimodal datasets, the framework can be tailored to capture the unique characteristics and relationships inherent in each task, thus broadening its applicability.