Current large multimodal models (LMMs) face challenges in grounding, which requires the model to relate language components to visual entities. Contrary to the common practice that fine-tunes LMMs with additional grounding supervision, we find that the grounding ability can in fact emerge in LMMs trained without explicit grounding supervision. To reveal this emerging grounding, we introduce an "attend-and-segment" method which analyzes the attention within standard LMMs to provide a point prompt to a segmentation model (e.g., SAM) and perform pixel-level segmentation. Furthermore, to enhance the grounding ability, we propose DiffLMM, an LMM utilizing a diffusion-based visual encoder, as opposed to the standard CLIP visual encoder, and trained with the same weak supervision. Without being constrained by the biases and limited scale of grounding-specific supervision data, our approach enables strong visual grounding while preserving general conversation abilities. We achieve competitive performance on both grounding-specific and general visual question answering benchmarks, compared with grounding LMMs and generalist LMMs, respectively. Notably, we achieve a 44.2 grounding mask recall on grounded conversation generation, outperforming the extensively supervised model GLaMM.
Keywords: Large Multimodal Model, Foundation Model, Visual Grounding, Weakly Supervised Learning
TL;DR: We discover the emergence of the grounding ability in large multimodal models trained without grounding supervision.
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Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 2465
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