Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection

• URL: http://arxiv.org/abs/2008.07064v1
• Date: Mon, 17 Aug 2020 02:55:06 GMT
• Title: Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection
• Authors: Shuhan Chen, Yun Fu
• Abstract summary: We aim to develop an efficient and compact deep network for RGB-D salient object detection. We propose a progressively guided alternate refinement network to refine it. Our model outperforms existing state-of-the-art approaches by a large margin.
• Score: 63.18846475183332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios. Starting from a coarse initial prediction by a multi-scale residual block, we propose a progressively guided alternate refinement network to refine it. Instead of using ImageNet pre-trained backbone network, we first construct a lightweight depth stream by learning from scratch, which can extract complementary features more efficiently with less redundancy. Then, different from the existing fusion based methods, RGB and depth features are fed into proposed guided residual (GR) blocks alternately to reduce their mutual degradation. By assigning progressive guidance in the stacked GR blocks within each side-output, the false detection and missing parts can be well remedied. Extensive experiments on seven benchmark datasets demonstrate that our model outperforms existing state-of-the-art approaches by a large margin, and also shows superiority in efficiency (71 FPS) and model size (64.9 MB).

Related papers

• RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery [72.13154206106259]
  We propose a novel pipeline that decouples the 6D pose and size estimation to mitigate the influence of imperfect scales on rigid transformations. Specifically, we leverage a pre-trained monocular estimator to extract local geometric information. A separate branch is designed to directly recover the metric scale of the object based on category-level statistics.
  arXiv  Detail & Related papers  (2023-09-19T02:20:26Z)
• AGG-Net: Attention Guided Gated-convolutional Network for Depth Image Completion [1.8820731605557168]
  We propose a new model for depth image completion based on the Attention Guided Gated-convolutional Network (AGG-Net) In the encoding stage, an Attention Guided Gated-Convolution (AG-GConv) module is proposed to realize the fusion of depth and color features at different scales. In the decoding stage, an Attention Guided Skip Connection (AG-SC) module is presented to avoid introducing too many depth-irrelevant features to the reconstruction.
  arXiv  Detail & Related papers  (2023-09-04T14:16:08Z)
• Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution [52.582632746409665]
  We propose a novel Symmetric Uncertainty-aware Feature Transmission (SUFT) for color-guided DSR. Our method achieves superior performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-01T06:35:59Z)
• Pyramidal Attention for Saliency Detection [30.554118525502115]
  This paper exploits only RGB images, estimates depth from RGB, and leverages the intermediate depth features. We employ a pyramidal attention structure to extract multi-level convolutional-transformer features to process initial stage representations. We report significantly improved performance against 21 and 40 state-of-the-art SOD methods on eight RGB and RGB-D datasets.
  arXiv  Detail & Related papers  (2022-04-14T06:57:46Z)
• Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images [42.26124628784883]
  We propose a compact, efficient, and end-to-end learning-based framework, namely AGD-Net. We first formulate the problem explicitly based on the classic gradient descent algorithm. AGD-Net can improve the reconstruction quality by more than 1.0 dB on average.
  arXiv  Detail & Related papers  (2021-08-12T05:54:09Z)
• 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)
• MobileSal: Extremely Efficient RGB-D Salient Object Detection [62.04876251927581]
  This paper introduces a novel network, methodname, which focuses on efficient RGB-D salient object detection (SOD) We propose an implicit depth restoration (IDR) technique to strengthen the feature representation capability of mobile networks for RGB-D SOD. With IDR and CPR incorporated, methodnameperforms favorably against sArt methods on seven challenging RGB-D SOD datasets.
  arXiv  Detail & Related papers  (2020-12-24T04:36:42Z)
• A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection [89.88222217065858]
  We design a single stream network to use the depth map to guide early fusion and middle fusion between RGB and depth. This model is 55.5% lighter than the current lightest model and runs at a real-time speed of 32 FPS when processing a $384 times 384$ image.
  arXiv  Detail & Related papers  (2020-07-14T04:40:14Z)

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.