The proposed Q-Perturbation can be extended to handle complex object relationships and scene compositions by incorporating a multi-object interaction framework. This could involve the following strategies:
Graph-Based Representations: By modeling the relationships between detected objects as a graph, where nodes represent objects and edges represent relationships (e.g., spatial, functional), the Q-Perturbation can leverage this structure to enhance queries based on the context of multiple objects. This would allow the model to understand not just individual objects but also how they interact within a scene.
Attention Mechanisms: Enhancing the cross-attention layers to consider not only the individual object features but also the collective features of groups of objects can improve the model's ability to capture complex interactions. For instance, using a hierarchical attention mechanism could allow the model to first focus on groups of related objects before refining its attention to individual objects.
Contextual Embeddings: Integrating contextual embeddings that capture the scene's overall semantics can help the model understand how objects relate to one another within a broader context. This could involve training on datasets that emphasize scene understanding, where relationships between objects are annotated.
Dynamic Query Perturbation: Instead of static perturbation based on individual object features, a dynamic approach could adjust the perturbation based on the detected relationships and the scene context. This would allow the model to adaptively enhance queries based on the complexity of the scene composition.
By implementing these strategies, the Q-Perturbation framework can evolve to better understand and retrieve information from complex scenes, ultimately improving its performance in cross-modal image-text retrieval tasks.