Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance

• URL: http://arxiv.org/abs/2408.15063v4
• Date: Tue, 12 Nov 2024 14:29:09 GMT
• Title: Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance
• Authors: Kunpeng Wang, Danying Lin, Chenglong Li, Zhengzheng Tu, Bin Luo,
• Abstract summary: We propose a novel framework to explore and exploit the powerful feature representation and zero-shot generalization ability of the Segment Anything Model (SAM) for multi-modal salient object detection (SOD) We develop underlineSAM with seunderlinemantic funderlineeature fuunderlinesion guidancunderlinee (Sammese) In the image encoder, a multi-modal adapter is proposed to adapt the single-modal SAM to multi-modal information. Specifically, in the mask decoder, a semantic-geometric
• Score: 15.435695491233982
• License:
• Abstract: Although most existing multi-modal salient object detection (SOD) methods demonstrate effectiveness through training models from scratch, the limited multi-modal data hinders these methods from reaching optimality. In this paper, we propose a novel framework to explore and exploit the powerful feature representation and zero-shot generalization ability of the pre-trained Segment Anything Model (SAM) for multi-modal SOD. Despite serving as a recent vision fundamental model, driving the class-agnostic SAM to comprehend and detect salient objects accurately is non-trivial, especially in challenging scenes. To this end, we develop \underline{SAM} with se\underline{m}antic f\underline{e}ature fu\underline{s}ion guidanc\underline{e} (Sammese), which incorporates multi-modal saliency-specific knowledge into SAM to adapt SAM to multi-modal SOD tasks. However, it is difficult for SAM trained on single-modal data to directly mine the complementary benefits of multi-modal inputs and comprehensively utilize them to achieve accurate saliency prediction. To address these issues, we first design a multi-modal complementary fusion module to extract robust multi-modal semantic features by integrating information from visible and thermal or depth image pairs. Then, we feed the extracted multi-modal semantic features into both the SAM image encoder and mask decoder for fine-tuning and prompting, respectively. Specifically, in the image encoder, a multi-modal adapter is proposed to adapt the single-modal SAM to multi-modal information. In the mask decoder, a semantic-geometric prompt generation strategy is proposed to produce corresponding embeddings with various saliency cues. Extensive experiments on both RGB-D and RGB-T SOD benchmarks show the effectiveness of the proposed framework. The code will be available at \url{https://github.com/Angknpng/Sammese}.

Related papers

• MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching [54.740256498985026]
  Keypoint detection and description methods often struggle with multimodal data. We propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching.
  arXiv  Detail & Related papers  (2025-01-20T06:56:30Z)
• MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt [60.10555128510744]
  Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities. Recently, large-scale pre-trained models like CLIP have demonstrated impressive performance in traditional single-modal object ReID tasks. We introduce a novel framework called MambaPro for multi-modal object ReID.
  arXiv  Detail & Related papers  (2024-12-14T06:33:53Z)
• Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts [17.6980007370549]
  We make the first attempt to adapt Segment Anything Model (SAM) for multi-modal semantic segmentation. By training only the MoE-LoRA layers while keeping SAM's weights frozen, SAM's strong generalization and segmentation capabilities can be preserved for downstream tasks. Specifically, to address cross-modal inconsistencies, we propose a novel MoE routing strategy that adaptively generates weighted features across modalities.
  arXiv  Detail & Related papers  (2024-12-05T14:54:31Z)
• Multimodality Helps Few-Shot 3D Point Cloud Semantic Segmentation [61.91492500828508]
  Few-shot 3D point cloud segmentation (FS-PCS) aims at generalizing models to segment novel categories with minimal support samples. We introduce a cost-free multimodal FS-PCS setup, utilizing textual labels and the potentially available 2D image modality. We propose a simple yet effective Test-time Adaptive Cross-modal Seg (TACC) technique to mitigate training bias.
  arXiv  Detail & Related papers  (2024-10-29T19:28:41Z)
• MANet: Fine-Tuning Segment Anything Model for Multimodal Remote Sensing Semantic Segmentation [8.443065903814821]
  This study introduces a novel Multimodal Adapter-based Network (MANet) for multimodal remote sensing semantic segmentation. At the core of this approach is the development of a Multimodal Adapter (MMAdapter), which fine-tunes SAM's image encoder to effectively leverage the model's general knowledge for multimodal data. This work not only introduces a novel network for multimodal fusion, but also demonstrates, for the first time, SAM's powerful generalization capabilities with Digital Surface Model (DSM) data.
  arXiv  Detail & Related papers  (2024-10-15T00:52:16Z)
• Segment Anything with Multiple Modalities [61.74214237816402]
  We develop MM-SAM, which 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. 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.
  arXiv  Detail & Related papers  (2024-08-17T03:45:40Z)
• Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection [58.241593208031816]
  Segment Anything Model (SAM) has been proposed as a visual fundamental model, which gives strong segmentation and generalization capabilities. We propose a Multi-scale and Detail-enhanced SAM (MDSAM) for Salient Object Detection (SOD) Experimental results demonstrate the superior performance of our model on multiple SOD datasets.
  arXiv  Detail & Related papers  (2024-08-08T09:09:37Z)
• Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment [11.897888221717245]
  This paper proposes a novel CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment. Our model is simple to implement without using task-specific external knowledge, and thus can easily migrate to other multi-modal tasks.
  arXiv  Detail & Related papers  (2024-03-11T01:07:36Z)
• FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing [88.6654909354382]
  We present a pure transformer-based framework, dubbed the Flexible Modal Vision Transformer (FM-ViT) for face anti-spoofing. FM-ViT can flexibly target any single-modal (i.e., RGB) attack scenarios with the help of available multi-modal data. Experiments demonstrate that the single model trained based on FM-ViT can not only flexibly evaluate different modal samples, but also outperforms existing single-modal frameworks by a large margin.
  arXiv  Detail & Related papers  (2023-05-05T04:28:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.