Centralized Information Interaction for Salient Object Detection

• URL: http://arxiv.org/abs/2012.11294v2
• Date: Thu, 24 Dec 2020 14:47:36 GMT
• Title: Centralized Information Interaction for Salient Object Detection
• Authors: Jiang-Jiang Liu, Zhi-Ang Liu, Ming-Ming Cheng
• Abstract summary: The U-shape structure has shown its advantage in salient object detection for efficiently combining multi-scale features. This paper shows that by centralizing these connections, we can achieve the cross-scale information interaction among them. Our approach can cooperate with various existing U-shape-based salient object detection methods by substituting the connections between the bottom-up and top-down pathways.
• Score: 68.8587064889475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The U-shape structure has shown its advantage in salient object detection for efficiently combining multi-scale features. However, most existing U-shape based methods focused on improving the bottom-up and top-down pathways while ignoring the connections between them. This paper shows that by centralizing these connections, we can achieve the cross-scale information interaction among them, hence obtaining semantically stronger and positionally more precise features. To inspire the potential of the newly proposed strategy, we further design a relative global calibration module that can simultaneously process multi-scale inputs without spatial interpolation. Benefiting from the above strategy and module, our proposed approach can aggregate features more effectively while introducing only a few additional parameters. Our approach can cooperate with various existing U-shape-based salient object detection methods by substituting the connections between the bottom-up and top-down pathways. Experimental results demonstrate that our proposed approach performs favorably against the previous state-of-the-arts on five widely used benchmarks with less computational complexity. The source code will be publicly available.

Related papers

• GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning [51.677086019209554]
  We propose a Generalized Structural Sparse to capture powerful relationships across modalities for pair-wise similarity learning. The distance metric delicately encapsulates two formats of diagonal and block-diagonal terms. Experiments on cross-modal and two extra uni-modal retrieval tasks have validated its superiority and flexibility.
  arXiv  Detail & Related papers  (2024-10-20T03:45:50Z)
• ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection [25.66305300362193]
  A novel feature fusion framework of dual cross-attention transformers is proposed to model global feature interaction. This framework enhances the discriminability of object features through the query-guided cross-attention mechanism. The proposed method achieves superior performance and faster inference, making it suitable for various practical scenarios.
  arXiv  Detail & Related papers  (2023-08-15T00:02:10Z)
• Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking [6.510588721127479]
  Experimental results demonstrate that a simple online MPN with these two contributions can perform better than many state-of-the-art methods. Our association method generalizes well and can also improve the results of private detection based methods.
  arXiv  Detail & Related papers  (2022-12-06T14:10:57Z)
• ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement [80.94378602238432]
  We propose an efficient structure named Correspondence Efficient Transformer (ECO-TR) by finding correspondences in a coarse-to-fine manner. To achieve this, multiple transformer blocks are stage-wisely connected to gradually refine the predicted coordinates. Experiments on various sparse and dense matching tasks demonstrate the superiority of our method in both efficiency and effectiveness against existing state-of-the-arts.
  arXiv  Detail & Related papers  (2022-09-25T13:05:33Z)
• MPI: Multi-receptive and Parallel Integration for Salient Object Detection [17.32228882721628]
  The semantic representation of deep features is essential for image context understanding. In this paper, a novel method called MPI is proposed for salient object detection. The proposed method outperforms state-of-the-art methods under different evaluation metrics.
  arXiv  Detail & Related papers  (2021-08-08T12:01:44Z)
• BiconNet: An Edge-preserved Connectivity-based Approach for Salient Object Detection [3.3517146652431378]
  We show that our model can use any existing saliency-based SOD framework as its backbone. Through comprehensive experiments on five benchmark datasets, we demonstrate that our proposed method outperforms state-of-the-art SOD approaches.
  arXiv  Detail & Related papers  (2021-02-27T21:39:04Z)
• Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks [62.26677215668959]
  Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. This paper proposes a wider'' network architecture which consists of parallel sub networks with totally different network architectures. Experiments on several famous benchmarks clearly demonstrate the superior performance, good generalization, and powerful learning ability of the proposed wider framework.
  arXiv  Detail & Related papers  (2020-08-07T10:39:11Z)
• Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints [80.60538408386016]
  Estimating relative camera poses from consecutive frames is a fundamental problem in visual odometry. We propose an end-to-end trainable framework consisting of learnable modules for detection, feature extraction, matching and outlier rejection.
  arXiv  Detail & Related papers  (2020-07-29T21:41:31Z)
• Multi-scale Interactive Network for Salient Object Detection [91.43066633305662]
  We propose the aggregate interaction modules to integrate the features from adjacent levels. To obtain more efficient multi-scale features, the self-interaction modules are embedded in each decoder unit. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-17T15:41:37Z)

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.