LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection

• URL: http://arxiv.org/abs/2411.06652v1
• Date: Mon, 11 Nov 2024 01:37:32 GMT
• Title: LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection
• Authors: Zhengyi Liu, Longzhen Wang, Xianyong Fang, Zhengzheng Tu, Linbo Wang,
• Abstract summary: A light field camera can reconstruct 3D scenes using captured multi-focus images that contain rich spatial geometric information. In this work, a state-of-the-art salient object detection model for multi-focus light field images, called LFSamba, is introduced.
• Score: 9.787855464038673
• License:
• Abstract: A light field camera can reconstruct 3D scenes using captured multi-focus images that contain rich spatial geometric information, enhancing applications in stereoscopic photography, virtual reality, and robotic vision. In this work, a state-of-the-art salient object detection model for multi-focus light field images, called LFSamba, is introduced to emphasize four main insights: (a) Efficient feature extraction, where SAM is used to extract modality-aware discriminative features; (b) Inter-slice relation modeling, leveraging Mamba to capture long-range dependencies across multiple focal slices, thus extracting implicit depth cues; (c) Inter-modal relation modeling, utilizing Mamba to integrate all-focus and multi-focus images, enabling mutual enhancement; (d) Weakly supervised learning capability, developing a scribble annotation dataset from an existing pixel-level mask dataset, establishing the first scribble-supervised baseline for light field salient object detection.https://github.com/liuzywen/LFScribble

Related papers

• Interactive Masked Image Modeling for Multimodal Object Detection in Remote Sensing [2.0528748158119434]
  multimodal learning can be used to integrate features from different data modalities, thereby improving detection accuracy. In this paper, we propose to use Masked Image Modeling (MIM) as a pre-training technique, leveraging self-supervised learning on unlabeled data. To address this, we propose a new interactive MIM method that can establish interactions between different tokens, which is particularly beneficial for object detection in remote sensing.
  arXiv  Detail & Related papers  (2024-09-13T14:50:50Z)
• PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection [59.355022416218624]
  integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. We propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN) PVAFN uses a multi-pooling strategy to integrate both multi-scale and region-specific information effectively.
  arXiv  Detail & Related papers  (2024-08-26T19:43:01Z)
• A Novel State Space Model with Local Enhancement and State Sharing for Image Fusion [14.293042131263924]
  In image fusion tasks, images from different sources possess distinct characteristics. Mamba, as a state space model, has emerged in the field of natural language processing. Motivated by these challenges, we customize and improve the vision Mamba network designed for the image fusion task.
  arXiv  Detail & Related papers  (2024-04-14T16:09:33Z)
• OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation [48.828453331724965]
  We propose an Omni-Aperture Fusion model (OAFuser) to extract angular information from sub-aperture images to generate semantically consistent results. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of all evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-28T14:43:27Z)
• SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and Multi-View for 3D Object Retrieval [8.74845857766369]
  Multi-modality 3D object retrieval is rarely developed and analyzed on large-scale datasets. We propose self-and-cross attention based aggregation of point cloud and multi-view images (SCA-PVNet) for 3D object retrieval.
  arXiv  Detail & Related papers  (2023-07-20T05:46:32Z)
• Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360{\deg} Omnidirectional Image [141.10227079090419]
  We propose a Multi-Projection Fusion and Refinement Network (MPFR-Net) to detect the salient objects in 360deg omnidirectional image. MPFR-Net uses the equirectangular projection image and four corresponding cube-unfolding images as inputs. Experimental results on two omnidirectional datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-12-23T14:50:40Z)
• A Simple Baseline for Multi-Camera 3D Object Detection [94.63944826540491]
  3D object detection with surrounding cameras has been a promising direction for autonomous driving. We present SimMOD, a Simple baseline for Multi-camera Object Detection. We conduct extensive experiments on the 3D object detection benchmark of nuScenes to demonstrate the effectiveness of SimMOD.
  arXiv  Detail & Related papers  (2022-08-22T03:38:01Z)
• Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration [56.01625477187448]
  We propose a MultiModality PAnoramic multi-object Tracking framework (MMPAT) It takes both 2D panorama images and 3D point clouds as input and then infers target trajectories using the multimodality data. We evaluate the proposed method on the JRDB dataset, where the MMPAT achieves the top performance in both the detection and tracking tasks.
  arXiv  Detail & Related papers  (2021-05-31T03:16:38Z)
• Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images [0.09065034043031667]
  We propose a novel method called Dense Multiscale Feature Fusion Pyramid Networks(DMFFPN), which is aimed at obtaining rich features as much as possible. Specifically, the dense connection is designed to fully utilize the representation from the different convolutional layers. Experiments on the drone-based datasets named VisDrone-DET suggest a competitive performance of our method.
  arXiv  Detail & Related papers  (2020-12-19T10:05:31Z)
• Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth [58.226535803985804]
  We introduce a large and realistic multi-focus dataset called Real-MFF. The dataset contains 710 pairs of source images with corresponding ground truth images. We evaluate 10 typical multi-focus algorithms on this dataset for the purpose of illustration.
  arXiv  Detail & Related papers  (2020-03-28T12:33:46Z)

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.