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Shifting from bounding boxes to pixel-level segmentation in MLLMs leads to significant gains in visual reasoning accuracy and segmentation performance.
Conventional multimodal learning fails to capture dynamic interactions, but DMIL adapts to sample-specific nuances, leading to superior performance across tasks.
