Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection

• URL: http://arxiv.org/abs/2201.09574v1
• Date: Mon, 24 Jan 2022 10:33:00 GMT
• Title: Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection
• Authors: Ze-yu Liu, Jian-wei Liu, Xin Zuo, Ming-fei Hu
• Abstract summary: salient visual cues appear in various scales and resolutions of RGB images due to semantic gaps at different feature levels. Similar salient patterns are available in cross-modal depth images as well as multi-scale versions. We devise attention based fusion module (ABF) to address on cross-modal correlation.
• Score: 7.062058947498447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The extensive research leveraging RGB-D information has been exploited in salient object detection. However, salient visual cues appear in various scales and resolutions of RGB images due to semantic gaps at different feature levels. Meanwhile, similar salient patterns are available in cross-modal depth images as well as multi-scale versions. Cross-modal fusion and multi-scale refinement are still an open problem in RGB-D salient object detection task. In this paper, we begin by introducing top-down and bottom-up iterative refinement architecture to leverage multi-scale features, and then devise attention based fusion module (ABF) to address on cross-modal correlation. We conduct extensive experiments on seven public datasets. The experimental results show the effectiveness of our devised method

Related papers

• A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network [9.855404465174455]
  Multiscale convolutional neural network (CNN) has demonstrated remarkable capabilities in solving various vision problems. We propose a customized feature fusion module, called Saliency Enhanced Feature Fusion (SEFF), for RGB-D saliency detection.
  arXiv  Detail & Related papers  (2024-01-22T13:01:35Z)
• Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection [20.12812979315803]
  Object detection utilizing both visible (RGB) and thermal infrared (IR) imagery has garnered extensive attention. Most existing multi-modal object detection methods directly input the RGB and IR images into deep neural networks. We propose a novel coarse-to-fine perspective to purify and fuse features from both modalities.
  arXiv  Detail & Related papers  (2024-01-19T14:49:42Z)
• HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness [2.341385717236931]
  We propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multi-granularity properties of geometric priors correlate well with the neural network hierarchies. Our HiDAnet performs favorably over the state-of-the-art methods by large margins.
  arXiv  Detail & Related papers  (2023-01-18T10:00:59Z)
• Interactive Context-Aware Network for RGB-T Salient Object Detection [7.544240329265388]
  We propose a novel network called Interactive Context-Aware Network (ICANet) ICANet contains three modules that can effectively perform the cross-modal and cross-scale fusions. Experiments prove that our network performs favorably against the state-of-the-art RGB-T SOD methods.
  arXiv  Detail & Related papers  (2022-11-11T10:04:36Z)
• Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection [67.33924278729903]
  In this work, we propose Dual Swin-Transformer based Mutual Interactive Network. We adopt Swin-Transformer as the feature extractor for both RGB and depth modality to model the long-range dependencies in visual inputs. Comprehensive experiments on five standard RGB-D SOD benchmark datasets demonstrate the superiority of the proposed DTMINet method.
  arXiv  Detail & Related papers  (2022-06-07T08:35:41Z)
• ObjectFormer for Image Manipulation Detection and Localization [118.89882740099137]
  We propose ObjectFormer to detect and localize image manipulations. We extract high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-03-28T12:27:34Z)
• Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection [78.47767202232298]
  We propose a novel Cross-modality Discrepant Interaction Network (CDINet) for RGB-D SOD. Two components are designed to implement the effective cross-modality interaction. Our network outperforms $15$ state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2021-08-04T11:24:42Z)
• Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion [15.033234579900657]
  RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. We propose a depth-sensitive RGB feature modeling scheme using the depth-wise geometric prior of salient objects. Experiments on seven standard benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.
  arXiv  Detail & Related papers  (2021-03-22T13:28:45Z)
• Self-Supervised Representation Learning for RGB-D Salient Object Detection [93.17479956795862]
  We use Self-Supervised Representation Learning to design two pretext tasks: the cross-modal auto-encoder and the depth-contour estimation. Our pretext tasks require only a few and un RGB-D datasets to perform pre-training, which make the network capture rich semantic contexts. For the inherent problem of cross-modal fusion in RGB-D SOD, we propose a multi-path fusion module.
  arXiv  Detail & Related papers  (2021-01-29T09:16:06Z)
• A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images [82.87122287748791]
  We propose a novel Parallel Down-up Fusion network (PDF-Net) for salient object detection in optical remote sensing images (RSIs) It takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds. Experiments on the ORSSD dataset demonstrate that the proposed network is superior to the state-of-the-art approaches both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2020-10-02T05:27:57Z)
• Cross-Modal Weighting Network for RGB-D Salient Object Detection [76.0965123893641]
  We propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD. Specifically, three RGB-depth interaction modules, named CMW-L, CMW-M and CMW-H, are developed to deal with respectively low-, middle- and high-level cross-modal information fusion. CMWNet consistently outperforms 15 state-of-the-art RGB-D SOD methods on seven popular benchmarks.
  arXiv  Detail & Related papers  (2020-07-09T16:01:44Z)

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.