The unified modeling approach of Show-o, which effectively integrates autoregressive and diffusion modeling for text and image modalities, can be extended to handle additional modalities such as audio and video through several strategies.
Tokenization Framework: Similar to how Show-o employs discrete tokens for text and images, audio can be tokenized into discrete representations using techniques like Mel-frequency cepstral coefficients (MFCCs) or spectrograms. This would allow the model to process audio data in a manner consistent with its handling of text and images.
Unified Attention Mechanism: The omni-attention mechanism utilized in Show-o can be adapted to accommodate audio and video tokens. By defining specific attention patterns for audio (e.g., causal attention for sequential audio data) and video (e.g., full attention for frames), the model can effectively learn inter-modal relationships while maintaining the integrity of each modality's unique characteristics.
Multimodal Training Objectives: To incorporate audio and video, Show-o can be trained with additional objectives that focus on tasks relevant to these modalities, such as audio captioning or video summarization. This would involve creating a diverse dataset that includes audio-visual pairs, allowing the model to learn from a broader range of multimodal interactions.
Hierarchical Modeling: For video, which is inherently a sequence of images, a hierarchical approach can be employed where the model first processes individual frames (as images) and then integrates temporal information across frames. This could involve using recurrent layers or attention mechanisms that account for the temporal dynamics of video data.
By implementing these strategies, Show-o's unified modeling approach can be effectively extended to encompass audio and video modalities, enhancing its versatility and applicability across a wider range of multimodal tasks.