Edge-Aware Mirror Network for Camouflaged Object Detection

• URL: http://arxiv.org/abs/2307.03932v1
• Date: Sat, 8 Jul 2023 08:14:49 GMT
• Title: Edge-Aware Mirror Network for Camouflaged Object Detection
• Authors: Dongyue Sun, Shiyao Jiang, Lin Qi
• Abstract summary: We propose a novel Edge-aware Mirror Network (EAMNet) to model edge detection and camouflaged object segmentation. EAMNet has a two-branch architecture, where a segmentation-induced edge aggregation module and an edge-induced integrity aggregation module are designed to cross-guide the segmentation branch and edge detection branch. Experiment results show that EAMNet outperforms existing cutting-edge baselines on three widely used COD datasets.
• Score: 5.032585246295627
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Existing edge-aware camouflaged object detection (COD) methods normally output the edge prediction in the early stage. However, edges are important and fundamental factors in the following segmentation task. Due to the high visual similarity between camouflaged targets and the surroundings, edge prior predicted in early stage usually introduces erroneous foreground-background and contaminates features for segmentation. To tackle this problem, we propose a novel Edge-aware Mirror Network (EAMNet), which models edge detection and camouflaged object segmentation as a cross refinement process. More specifically, EAMNet has a two-branch architecture, where a segmentation-induced edge aggregation module and an edge-induced integrity aggregation module are designed to cross-guide the segmentation branch and edge detection branch. A guided-residual channel attention module which leverages the residual connection and gated convolution finally better extracts structural details from low-level features. Quantitative and qualitative experiment results show that EAMNet outperforms existing cutting-edge baselines on three widely used COD datasets. Codes are available at https://github.com/sdy1999/EAMNet.

Related papers

• LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion [79.22197702626542]
  This paper introduces a framework that explores amodal segmentation for robotic grasping in cluttered scenes. We propose a Linear-fusion Attention-guided Convolutional Network (LAC-Net) The results on different datasets show that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-08-06T14:50:48Z)
• ECT: Fine-grained Edge Detection with Learned Cause Tokens [19.271691951077617]
  We propose a two-stage transformer-based network sequentially predicting generic edges and fine-grained edges. We evaluate our method on the public benchmark BSDS-RIND and several newly derived benchmarks, and achieve new state-of-the-art results.
  arXiv  Detail & Related papers  (2023-08-06T11:37:55Z)
• Edge-aware Plug-and-play Scheme for Semantic Segmentation [4.297988192695948]
  The proposed method can be seamlessly integrated into any state-of-the-art (SOTA) models with zero modification. The experimental results indicate that the proposed method can be seamlessly integrated into any state-of-the-art (SOTA) models with zero modification.
  arXiv  Detail & Related papers  (2023-03-18T02:17:37Z)
• End-to-End Instance Edge Detection [29.650295133113183]
  Edge detection has long been an important problem in the field of computer vision. Previous works have explored category-agnostic or category-aware edge detection. In this paper, we explore edge detection in the context of object instances.
  arXiv  Detail & Related papers  (2022-04-06T15:32:21Z)
• Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network [17.538512222905087]
  This paper proposes a novel edge-based reversible re-calibration network called ERRNet. Our model is characterized by two innovative designs, namely Selective Edge Aggregation (SEA) and Reversible Re-calibration Unit (RRU) Experimental results show that ERRNet outperforms existing cutting-edge baselines on three COD datasets and five medical image segmentation datasets.
  arXiv  Detail & Related papers  (2021-11-05T02:03:54Z)
• Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation [95.74244714914052]
  Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich-temporal information online. PCAN outperforms current video instance tracking and segmentation competition winners on Youtube-VIS and BDD100K datasets.
  arXiv  Detail & Related papers  (2021-06-22T17:57:24Z)
• Enhanced Boundary Learning for Glass-like Object Segmentation [55.45473926510806]
  This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module for generating finer boundary cues. An edge-aware point-based graph convolution network module is proposed to model the global shape representation along the boundary.
  arXiv  Detail & Related papers  (2021-03-29T16:18:57Z)
• Boundary-Aware Segmentation Network for Mobile and Web Applications [60.815545591314915]
  Boundary-Aware Network (BASNet) is integrated with a predict-refine architecture and a hybrid loss for highly accurate image segmentation. BASNet runs at over 70 fps on a single GPU which benefits many potential real applications. Based on BASNet, we further developed two (close to) commercial applications: AR COPY & PASTE, in which BASNet is augmented reality for "COPY" and "PASTING" real-world objects, and OBJECT CUT, which is a web-based tool for automatic object background removal.
  arXiv  Detail & Related papers  (2021-01-12T19:20:26Z)
• Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection [63.942632088208505]
  We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects.
  arXiv  Detail & Related papers  (2020-03-28T18:51:43Z)
• Saliency Enhancement using Gradient Domain Edges Merging [65.90255950853674]
  We develop a method to merge the edges with the saliency maps to improve the performance of the saliency. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of at least 3.4 times higher on the DUT-OMRON dataset. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.
  arXiv  Detail & Related papers  (2020-02-11T14:04:56Z)

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.