The prefixLM + generative retrieval framework can be effectively extended to other visual reasoning tasks, such as visual relation detection and scene graph generation, by leveraging its inherent ability to model complex dependencies between visual elements. For visual relation detection, the framework can be adapted to generate sentences that describe the relationships between pairs of objects in an image. By constructing sentence templates that explicitly incorporate relational phrases (e.g., “{O1} is next to {O2}” or “{O1} interacts with {O2}”), the generative retrieval approach can capture the nuances of spatial and functional relationships between objects.
In the context of scene graph generation, the framework can be utilized to create a structured representation of an image, where nodes represent objects and edges represent relationships. By employing a similar generative retrieval strategy, the model can output a graph structure that encodes both object attributes and their interrelations. This can be achieved by training the prefixLM to generate graph representations based on visual inputs, allowing for the extraction of rich contextual information that informs the relationships between objects.
Moreover, the flexibility of the generative retrieval method allows for the incorporation of various sentence templates that can be tailored to specific tasks, enhancing the model's ability to generalize across different visual reasoning challenges. By refining the templates to include relational and contextual cues, the framework can be adapted to a wide range of visual reasoning applications, thereby broadening its utility in the field of computer vision.