# Visual Consensus Prompting for Co-Salient Object Detection

arXiv: https://arxiv.org/abs/2504.14254 · HTML: https://mlsanity.com/paper/2504.14254/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 53 references that are in the map.
- First posted on arXiv 19 Apr 2025.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (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)
- [Salient Object Detection in the Deep Learning Era: An In-Depth Survey](https://mlsanity.com/paper/1904.09146/) (2019)
- [Visual Prompt Multi-Modal Tracking](https://mlsanity.com/paper/2303.10826/) (2023)
- [Explicit Visual Prompting for Low-Level Structure Segmentations](https://mlsanity.com/paper/2303.10883/) (2023)
- [VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning](https://mlsanity.com/paper/2311.15011/) (2023)
- [Re-thinking Co-Salient Object Detection](https://mlsanity.com/paper/2007.03380/) (2020)
- [Pro-tuning: Unified Prompt Tuning for Vision Tasks](https://mlsanity.com/paper/2207.14381/) (2022)
- [Gradient-Induced Co-Saliency Detection](https://mlsanity.com/paper/2004.13364/) (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)
- [HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images](https://mlsanity.com/paper/1811.06679/) (2018)
- [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)
- [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)
- [Co-Salient Object Detection with Semantic-Level Consensus Extraction and Dispersion](https://mlsanity.com/paper/2309.07753/) (2023)
- [CONDA: Condensed Deep Association Learning for Co-Salient Object Detection](https://mlsanity.com/paper/2409.01021/) (2024)

## Built on by (most cited first, 3 of 3)

- [Follow the Saliency: Supervised Saliency for Retrieval-augmented Dense Video Captioning](https://mlsanity.com/paper/2603.11460/) (2026)
- [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)

Existing co-salient object detection (CoSOD) methods generally employ a three-stage architecture (i.e., encoding, consensus extraction & dispersion, and prediction) along with a typical full fine-tuning paradigm. Although they yield certain benefits, they exhibit two notable limitations: 1) This architecture relies on encoded features to facilitate consensus extraction, but the meticulously extracted consensus does not provide timely guidance to the encoding stage. 2) This paradigm involves globally updating all parameters of the model, which is parameter-inefficient and hinders the effective representation of knowledge within the foundation model for this task. Therefore, in this paper, we propose an interaction-effective and parameter-efficient concise architecture for the CoSOD task, addressing two key limitations. It introduces, for the first time, a parameter-efficient prompt tuning paradigm and seamlessly embeds consensus into the prompts to formulate task-specific Visual Consensus Prompts (VCP). Our VCP aims to induce the frozen foundation model to perform better on CoSOD tasks by formulating task-specific visual consensus prompts with minimized tunable parameters. Concretely, the primary insight of the purposeful Consensus Prompt Generator (CPG) is to enforce limited tunable parameters to focus on co-salient representations and generate consensus prompts. The formulated Consensus Prompt Disperser (CPD) leverages consensus prompts to form task-specific visual consensus prompts, thereby arousing the powerful potential of pre-trained models in addressing CoSOD tasks. Extensive experiments demonstrate that our concise VCP outperforms 13 cutting-edge full fine-tuning models, achieving the new state of the art (with 6.8% improvement in F_m metrics on the most challenging CoCA dataset). Source code has been available at https://github.com/WJ-CV/VCP.