The EAGLE model can be extended to accommodate more complex visual prompts, such as freeform sketches or textual annotations, by integrating additional preprocessing and encoding mechanisms that can interpret these diverse input formats. One approach could involve developing a sketch recognition module that utilizes convolutional neural networks (CNNs) or transformer-based architectures to analyze and understand the semantics of freeform sketches. This module would convert sketches into structured representations that the EAGLE model can process, similar to how it currently handles colored patches.
Additionally, for textual annotations, the model could incorporate a natural language processing (NLP) component that interprets the context and intent behind the text. This could involve using embeddings from pre-trained language models to capture the semantic meaning of the annotations, allowing the EAGLE model to align these textual inputs with the corresponding visual features effectively. By combining these enhancements with the existing Geometry-Agnostic Learning (GAL) paradigm, EAGLE could achieve a more robust understanding of complex visual prompts, thereby improving its performance in real-world applications where users may provide varied and intricate instructions.