FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation

• URL: http://arxiv.org/abs/2408.13980v1
• Date: Mon, 26 Aug 2024 02:20:55 GMT
• Title: FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation
• Authors: Daixun Li, Weiying Xie, Mingxiang Cao, Yunke Wang, Jiaqing Zhang, Yunsong Li, Leyuan Fang, Chang Xu,
• Abstract summary: We introduce SAM into multimodal image segmentation for the first time. We propose a novel framework that combines Latent Space Token Generation (LSTG) and Fusion Mask Prompting (FMP) modules to enhance SAM's multimodal fusion and segmentation capabilities.
• Score: 37.74045675588487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal image fusion and segmentation enhance scene understanding in autonomous driving by integrating data from various sensors. However, current models struggle to efficiently segment densely packed elements in such scenes, due to the absence of comprehensive fusion features that can guide mid-process fine-tuning and focus attention on relevant areas. The Segment Anything Model (SAM) has emerged as a transformative segmentation method. It provides more effective prompts through its flexible prompt encoder, compared to transformers lacking fine-tuned control. Nevertheless, SAM has not been extensively studied in the domain of multimodal fusion for natural images. In this paper, we introduce SAM into multimodal image segmentation for the first time, proposing a novel framework that combines Latent Space Token Generation (LSTG) and Fusion Mask Prompting (FMP) modules to enhance SAM's multimodal fusion and segmentation capabilities. Specifically, we first obtain latent space features of the two modalities through vector quantization and embed them into a cross-attention-based inter-domain fusion module to establish long-range dependencies between modalities. Then, we use these comprehensive fusion features as prompts to guide precise pixel-level segmentation. Extensive experiments on several public datasets demonstrate that the proposed method significantly outperforms SAM and SAM2 in multimodal autonomous driving scenarios, achieving at least 3.9$\%$ higher segmentation mIoU than the state-of-the-art approaches.

Related papers

• X-SAM: From Segment Anything to Any Segmentation [63.79182974315084]
  Large Language Models (LLMs) demonstrate strong capabilities in broad knowledge representation, yet they are inherently deficient in pixel-level perceptual understanding.<n>We present X-SAM, a streamlined Multimodal Large Language Model framework that extends the segmentation paradigm from textitsegment anything to textitany segmentation.<n>We propose a new segmentation task, termed Visual GrounDed (VGD) segmentation, which segments all instance objects with interactive visual prompts and empowers MLLMs with visual grounded, pixel-wise interpretative capabilities.
  arXiv  Detail & Related papers  (2025-08-06T17:19:10Z)
• DSU-Net:An Improved U-Net Model Based on DINOv2 and SAM2 with Multi-scale Cross-model Feature Enhancement [7.9006143460465355]
  This paper proposes a multi-scale feature collabora-tion framework guided by DINOv2 for SAM2, with core innovations in three aspects. It surpasses existing state-of-the-art meth-ods in downstream tasks such as camouflage target detection and salient ob-ject detection, without requiring costly training processes.
  arXiv  Detail & Related papers  (2025-03-27T06:08:24Z)
• MAGIC++: Efficient and Resilient Modality-Agnostic Semantic Segmentation via Hierarchical Modality Selection [20.584588303521496]
  We introduce the MAGIC++ framework, which comprises two key plug-and-play modules for effective multi-modal fusion and hierarchical modality selection.<n>Our method achieves state-of-the-art performance on both real-world and synthetic benchmarks.<n>Our method is superior in the novel modality-agnostic setting, where it outperforms prior arts by a large margin.
  arXiv  Detail & Related papers  (2024-12-22T06:12:03Z)
• 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.<n>We introduce a multimodal FS-PCS setup, utilizing textual labels and the potentially available 2D image modality.<n>We propose a simple yet effective Test-time Adaptive Cross-modal (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)
• Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance [15.435695491233982]
  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
  arXiv  Detail & Related papers  (2024-08-27T13:47:31Z)
• SAM-REF: Introducing Image-Prompt Synergy during Interaction for Detail Enhancement in the Segment Anything Model [24.774935133996074]
  We propose a two-stage refinement framework that fully integrates images and prompts by using a lightweight refiner. Our SAM-REF model outperforms the current state-of-the-art method in most metrics on segmentation quality without compromising efficiency.
  arXiv  Detail & Related papers  (2024-08-21T11:18:35Z)
• 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)
• U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation [63.31007867379312]
  We introduce U3M: An Unbiased Multiscale Modal Fusion Model for Multimodal Semantics. We employ feature fusion at multiple scales to ensure the effective extraction and integration of both global and local features. Experimental results demonstrate that our approach achieves superior performance across multiple datasets.
  arXiv  Detail & Related papers  (2024-05-24T08:58:48Z)
• RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything [117.02741621686677]
  This work explores a novel real-time segmentation setting called real-time multi-purpose segmentation.<n>It contains three fundamental sub-tasks: interactive segmentation, panoptic segmentation, and video instance segmentation.<n>We present a novel dynamic convolution-based method, Real-Time Multi-Purpose SAM (RMP-SAM)<n>It contains an efficient encoder and an efficient decoupled adapter to perform prompt-driven decoding.
  arXiv  Detail & Related papers  (2024-01-18T18:59:30Z)
• Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation [66.15246197473897]
  Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation. We propose a textbfMulti-textbfinteractive textbfFeature learning architecture for image fusion and textbfSegmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:03:58Z)
• Semantic-SAM: Segment and Recognize Anything at Any Granularity [83.64686655044765]
  We introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. We consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels.
  arXiv  Detail & Related papers  (2023-07-10T17:59:40Z)
• Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion [63.72912507445662]
  We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network. We verify that multimodal features can be learnt within a shared single network by merely maintaining modality-specific batch normalization layers in the encoder. Secondly, we propose a bidirectional multi-layer fusion scheme, where multimodal features can be exploited progressively.
  arXiv  Detail & Related papers  (2021-08-11T03:42:13Z)
• CMF: Cascaded Multi-model Fusion for Referring Image Segmentation [24.942658173937563]
  We address the task of referring image segmentation (RIS), which aims at predicting a segmentation mask for the object described by a natural language expression. We propose a simple yet effective Cascaded Multi-modal Fusion (CMF) module, which stacks multiple atrous convolutional layers in parallel. Experimental results on four benchmark datasets demonstrate that our method outperforms most state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-16T08:18:39Z)

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.