Segment Anything with Multiple Modalities
Keywords: segment anything, multimodal fusion, foundation model, parameter-efficient tuning, domain adaptation, unsupervised learning, weakly supervised learning
TL;DR: MM-SAM: enhancing SAM for sensor suite
Abstract: Robust and accurate segmentation of scenes has become one core functionality in various visual recognition and navigation tasks. This has inspired the recent development of Segment Anything Model (SAM), a foundation model for general mask segmentation. However, SAM is largely tailored for single-modal RGB images, limiting its applicability to multi-modal data captured with widely-adopted sensor suites, such as LiDAR plus RGB, depth plus RGB, thermal plus RGB, etc. We develop MM-SAM, an extension and expansion of SAM that supports cross-modal and multi-modal processing for robust and enhanced segmentation with different sensor suites. MM-SAM features two key designs, namely, unsupervised cross-modal transfer and weakly-supervised multi-modal fusion, enabling label-free and parameter-efficient adaptation toward various sensor modalities. It addresses three main challenges: 1) adaptation toward diverse non-RGB sensors for single-modal processing, 2) synergistic processing of multi-modal data via sensor fusion, and 3) mask-free training for different downstream tasks. Notably, we demonstrate that the output latent space of SAM's RGB image encoder can function as a highly abstract, shareable embedding space compatible with segmentation across different sensor modalities. Extensive experiments show that MM-SAM consistently outperforms SAM by large margins, demonstrating its effectiveness and robustness across various sensors and data modalities. Code will be released.
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 6974
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