Gradient-Induced Co-Saliency Detection

Published in

European Conference on Computer Vision(2020)

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TL;DR

A gradient-induced co-saliency detection (GICD) method that first abstracts a consensus representation for the grouped images in the embedding space; then, by comparing the single image with consensus representation, utilizes the feedback gradient information to induce more attention to the discriminative co- salient features.

Abstract

Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images. In this paper, inspired by human behavior, we propose a gradient-induced co-saliency detection (GICD) method. We first abstract a consensus representation for the grouped images in the embedding space; then, by comparing the single image with consensus representation, we utilize the feedback gradient information to induce more attention to the discriminative co-salient features. In addition, due to the lack of Co-SOD training data, we design a jigsaw training strategy, with which Co-SOD networks can be trained on general saliency datasets without extra pixel-level annotations. To evaluate the performance of Co-SOD methods on discovering the co-salient object among multiple foregrounds, we construct a challenging CoCA dataset, where each image contains at least one extraneous foreground along with the co-salient object. Experiments demonstrate that our GICD achieves state-of-the-art performance. Our codes and dataset are available at this https URL.

Authors

Ming-Ming Cheng

30 papers

Zhao Zhang

2 papers

Jun Xu

3 papers

References60 items

BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network

Taking a Deeper Look at Co-Salient Object Detection

Trustful Internet of Surveillance Things Based on Deeply Represented Visual Co-Saliency Detection

Highly Efficient Salient Object Detection with 100K Parameters

Robust Deep Co-Saliency Detection With Group Semantic and Pyramid Attention

Gradient-Induced Co-Saliency Detection

Published in

European Conference on Computer Vision(2020)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Ming-Ming Cheng

30 papers

Zhao Zhang

2 papers

Jun Xu

3 papers

References60 items

BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network

Taking a Deeper Look at Co-Salient Object Detection

Trustful Internet of Surveillance Things Based on Deeply Represented Visual Co-Saliency Detection

Highly Efficient Salient Object Detection with 100K Parameters

Robust Deep Co-Saliency Detection With Group Semantic and Pyramid Attention

PyTorch: An Imperative Style, High-Performance Deep Learning Library

A Unified Multiple Graph Learning and Convolutional Network Model for Co-saliency Estimation

Stacked Cross Refinement Network for Edge-Aware Salient Object Detection

Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation

SSAP: Single-Shot Instance Segmentation With Affinity Pyramid

Detecting Robust Co-Saliency with Recurrent Co-Attention Neural Network

Robust Deep Co-Saliency Detection with Group Semantic

Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing

BASNet: Boundary-Aware Salient Object Detection

A Simple Pooling-Based Design for Real-Time Salient Object Detection

Deep Group-Wise Fully Convolutional Network for Co-Saliency Detection With Graph Propagation

CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning

Efficient Video Object Co-Localization With Co-Saliency Activated Tracklets

Image Co-Saliency Detection and Co-Segmentation via Progressive Joint Optimization

One-Shot Instance Segmentation

SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation

A Feature-Adaptive Semi-Supervised Framework for Co-saliency Detection

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Unsupervised CNN-Based Co-saliency Detection with Graphical Optimization

Interactive Image Segmentation with Latent Diversity

Enhanced-alignment Measure for Binary Foreground Map Evaluation

Group Collaborative Representation for Image Set Classification

Exploring the Limits of Weakly Supervised Pretraining

Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground

A Review of Co-Saliency Detection Algorithms

Low-Shot Learning with Imprinted Weights

Spotting Trip Purposes from Taxi Trajectories

Structure-Measure: A New Way to Evaluate Foreground Maps

Group-wise Deep Co-saliency Detection

Learning to Detect Salient Objects with Image-Level Supervision

Learning Deep Match Kernels for Image-Set Classification

Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework

Feature Pyramid Networks for Object Detection

CATS: Co-saliency Activated Tracklet Selection for Video Co-localization

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Detection of Co-salient Objects by Looking Deep and Wide

Learning Deep Features for Discriminative Localization

Multi-Camera Saliency

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Salient Object Detection: A Benchmark

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Efficient Image and Video Co-localization with Frank-Wolfe Algorithm

Co-localization in Real-World Images

Self-Adaptively Weighted Co-Saliency Detection via Rank Constraint

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Cluster-Based Co-Saliency Detection

A Co-Saliency Model of Image Pairs

From co-saliency to co-segmentation: An efficient and fully unsupervised energy minimization model

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Field of Study

Computer Science

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

European Conference on Computer Vision

Type

conference

URL

https://link.springer.com/conference/eccv

Alternate Names

ECCV
Eur Conf Comput Vis