# Gradient-Induced Co-Saliency Detection

arXiv: https://arxiv.org/abs/2004.13364 · HTML: https://mlsanity.com/paper/2004.13364/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 30 of 39)

- [Bilateral Reference for High-Resolution Dichotomous Image Segmentation](https://mlsanity.com/paper/2401.03407/) (2024)
- [A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection](https://mlsanity.com/paper/2203.04708/) (2022)
- [Group Collaborative Learning for Co-Salient Object Detection](https://mlsanity.com/paper/2104.01108/) (2021)
- [Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection](https://mlsanity.com/paper/2110.00338/) (2021)
- [Global-and-Local Collaborative Learning for Co-Salient Object Detection](https://mlsanity.com/paper/2204.08917/) (2022)
- [Grounded Affordance from Exocentric View](https://mlsanity.com/paper/2208.13196/) (2022)
- [NamedMask: Distilling Segmenters from Complementary Foundation Models](https://mlsanity.com/paper/2209.11228/) (2022)
- [Referring Camouflaged Object Detection](https://mlsanity.com/paper/2306.07532/) (2023)
- [Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection](https://mlsanity.com/paper/2203.05787/) (2022)
- [Advancing Referring Expression Segmentation Beyond Single Image](https://mlsanity.com/paper/2305.12452/) (2023)
- [Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection](https://mlsanity.com/paper/2305.00514/) (2023)
- [GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector](https://mlsanity.com/paper/2205.15469/) (2022)
- [CoSformer: Detecting Co-Salient Object with Transformers](https://mlsanity.com/paper/2104.14729/) (2021)
- [Personalized Image Semantic Segmentation](https://mlsanity.com/paper/2107.13978/) (2021)
- [Memory-aided Contrastive Consensus Learning for Co-salient Object Detection](https://mlsanity.com/paper/2302.14485/) (2023)
- [Co-Salient Object Detection with Co-Representation Purification](https://mlsanity.com/paper/2303.07670/) (2023)
- [Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark](https://mlsanity.com/paper/2310.04253/) (2023)
- [G-VOILA: Gaze-Facilitated Information Querying in Daily Scenarios](https://mlsanity.com/paper/2405.07652/) (2024)
- [Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics](https://mlsanity.com/paper/2311.06654/) (2023)
- [Prototypical Kernel Learning and Open-set Foreground Perception for Generalized Few-shot Semantic Segmentation](https://mlsanity.com/paper/2308.04952/) (2023)
- [Co-Salient Object Detection with Semantic-Level Consensus Extraction and Dispersion](https://mlsanity.com/paper/2309.07753/) (2023)
- [Towards Stable Co-saliency Detection and Object Co-segmentation](https://mlsanity.com/paper/2209.12138/) (2022)
- [Zero-shot Unsupervised Transfer Instance Segmentation](https://mlsanity.com/paper/2304.14376/) (2023)
- [Zero-Shot Co-salient Object Detection Framework](https://mlsanity.com/paper/2309.05499/) (2023)
- [CONDA: Condensed Deep Association Learning for Co-Salient Object Detection](https://mlsanity.com/paper/2409.01021/) (2024)
- [Visual Consensus Prompting for Co-Salient Object Detection](https://mlsanity.com/paper/2504.14254/) (2025)
- [Generalised Co-Salient Object Detection](https://mlsanity.com/paper/2208.09668/) (2022)
- [CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection](https://mlsanity.com/paper/2403.18554/) (2024)
- [Free Lunch for Co-Saliency Detection: Context Adjustment](https://mlsanity.com/paper/2108.02093/) (2021)
- [A Unified Two-Stage Group Semantics Propagation and Contrastive Learning Network for Co-Saliency Detection](https://mlsanity.com/paper/2208.06615/) (2022)

## Abstract (arXiv)

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 https://mmcheng.net/gicd/.