# Co-Salient Object Detection with Semantic-Level Consensus Extraction and Dispersion

arXiv: https://arxiv.org/abs/2309.07753 · HTML: https://mlsanity.com/paper/2309.07753/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 48 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [Visual Saliency Transformer](https://mlsanity.com/paper/2104.12099/) (2021)
- [Re-thinking Co-Salient Object Detection](https://mlsanity.com/paper/2007.03380/) (2020)
- [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)
- [TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network](https://mlsanity.com/paper/2108.03990/) (2021)
- [Low-rank Bilinear Pooling for Fine-Grained Classification](https://mlsanity.com/paper/1611.05109/) (2016)
- [Gradient-Induced Co-Saliency Detection](https://mlsanity.com/paper/2004.13364/) (2020)
- [Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection](https://mlsanity.com/paper/2003.06167/) (2020)
- [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)
- [Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection](https://mlsanity.com/paper/2203.05787/) (2022)
- [Group-wise Deep Co-saliency Detection](https://mlsanity.com/paper/1707.07381/) (2017)
- [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)

## Built on by (most cited first, 6 of 6)

- [Language-Driven Visual Consensus for Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2403.08426/) (2024)
- [Neural Assembler: Learning to Generate Fine-Grained Robotic Assembly Instructions from Multi-View Images](https://mlsanity.com/paper/2404.16423/) (2024)
- [Visual Consensus Prompting for Co-Salient Object Detection](https://mlsanity.com/paper/2504.14254/) (2025)
- [Self-supervised co-salient object detection via feature correspondence at multiple scales](https://mlsanity.com/paper/2403.11107/) (2024)
- [TF-SSD: A Strong Pipeline via Synergic Mask Filter for Training-free Co-salient Object Detection](https://mlsanity.com/paper/2604.00549/) (2026)
- [Rank-Consistent Set Reasoning for Co-Salient Object Detection](https://mlsanity.com/paper/2609.13706/) (2026)

## Abstract (arXiv)

Given a group of images, co-salient object detection (CoSOD) aims to highlight the common salient object in each image. There are two factors closely related to the success of this task, namely consensus extraction, and the dispersion of consensus to each image. Most previous works represent the group consensus using local features, while we instead utilize a hierarchical Transformer module for extracting semantic-level consensus. Therefore, it can obtain a more comprehensive representation of the common object category, and exclude interference from other objects that share local similarities with the target object. In addition, we propose a Transformer-based dispersion module that takes into account the variation of the co-salient object in different scenes. It distributes the consensus to the image feature maps in an image-specific way while making full use of interactions within the group. These two modules are integrated with a ViT encoder and an FPN-like decoder to form an end-to-end trainable network, without additional branch and auxiliary loss. The proposed method is evaluated on three commonly used CoSOD datasets and achieves state-of-the-art performance.