Summary of Customize Segment Anything Model For Multi-modal Semantic Segmentation with Mixture Of Lora Experts, by Chenyang Zhu et al.
Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts
by Chenyang Zhu, Bin Xiao, Lin Shi, Shoukun Xu, Xu Zheng
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent Segment Anything Model (SAM) has shown strong performance in various downstream applications in the RGB modality. However, applying SAM directly to emerging visual modalities such as depth and event data results in suboptimal performance in multi-modal segmentation tasks. To address this limitation, a Mixture of Low-Rank Adaptation Experts (MoE-LoRA) is proposed to adapt SAM for multi-modal semantic segmentation. The MoE-LoRA layers are trained while keeping SAM’s weights frozen, preserving its strong generalization and segmentation capabilities. A novel MoE routing strategy is also introduced to address cross-modal inconsistencies and enhance multi-modal feature integration. Additionally, multi-scale feature extraction and fusion are incorporated by adapting SAM’s segmentation head and introducing an auxiliary segmentation head. Extensive experiments were conducted on three multi-modal benchmarks: DELIVER, MUSES, and MCubeS. The results consistently demonstrate that the proposed method significantly outperforms state-of-the-art approaches across diverse scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAM is a powerful model for RGB data, but it doesn’t work well with other types of visual data like depth or event information. To fix this, scientists created a new approach called MoE-LoRA that helps SAM understand different types of data better. This works by creating special layers that learn how to combine information from different sources. The team also came up with a way to make sure the model is considering all the right details at the right time. They tested their idea on lots of different datasets and found that it did much better than other methods. |
Keywords
» Artificial intelligence » Feature extraction » Generalization » Lora » Low rank adaptation » Multi modal » Sam » Semantic segmentation
