While the Geometry-Agnostic Learning (GAL) paradigm offers significant advantages in disentangling region-level recognition from the specific formats of referring visual prompts, it does have potential limitations. One limitation is its reliance on the assumption that the primary object of interest is adequately represented by the centroid or a single point within the region. In cases where objects are irregularly shaped or when the main subject is not centrally located, this approach may lead to suboptimal performance.
To improve GAL, future iterations could incorporate a more sophisticated sampling strategy that considers the distribution of pixels within the region. For instance, employing techniques such as spatial attention mechanisms could allow the model to focus on multiple salient points within a region, rather than just the centroid. Additionally, integrating a feedback loop where the model can iteratively refine its understanding based on user interactions or corrections could enhance its adaptability to diverse and challenging visual prompts.
Furthermore, expanding the training dataset to include a wider variety of visual prompts, including those with occlusions or overlapping objects, could help the model learn to generalize better across different scenarios. This would ensure that GAL remains effective even when faced with complex and unpredictable user inputs.