mlsanityData to 14 Oct 2026

Visual Consensus Prompting for Co-Salient Object Detection

arXiv 2504.14254 ↗First posted 19 Apr 2025cs.CVcited by 3

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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.

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

30 of 53 references.

References · 30

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  4. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  5. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  6. 2021Vision Transformers for Dense Predictioncited by 1,803
  7. 2022Visual Prompt Tuningcited by 1,681
  8. 2019How Can We Know What Language Models Know?cited by 959
  9. 2021PVT v2: Improved Baselines with Pyramid Vision Transformercited by 751
  10. 2022AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognitioncited by 719
  11. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 689
  12. 2017Structure-measure: A New Way to Evaluate Foreground Mapscited by 480
18 more
  1. 2018Enhanced-alignment Measure for Binary Foreground Map Evaluationcited by 370
  2. 2019Salient Object Detection in the Deep Learning Era: An In-Depth Surveycited by 164
  3. 2023Visual Prompt Multi-Modal Trackingcited by 142
  4. 2023Explicit Visual Prompting for Low-Level Structure Segmentationscited by 114
  5. 2023VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learningcited by 58
  6. 2020Re-thinking Co-Salient Object Detectioncited by 54
  7. 2022Pro-tuning: Unified Prompt Tuning for Vision Taskscited by 53
  8. 2020Gradient-Induced Co-Saliency Detectioncited by 39
  9. 2021Group Collaborative Learning for Co-Salient Object Detectioncited by 32
  10. 2021Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detectioncited by 24
  11. 2022Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detectioncited by 18
  12. 2018HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Imagescited by 15
  13. 2023Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detectioncited by 12
  14. 2022GCoNet+: A Stronger Group Collaborative Co-Salient Object Detectorcited by 11
  15. 2023Memory-aided Contrastive Consensus Learning for Co-salient Object Detectioncited by 9
  16. 2023Co-Salient Object Detection with Co-Representation Purificationcited by 9
  17. 2023Co-Salient Object Detection with Semantic-Level Consensus Extraction and Dispersioncited by 6
  18. 2024CONDA: Condensed Deep Association Learning for Co-Salient Object Detectioncited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Follow the Saliency: Supervised Saliency for Retrieval-augmented Dense Video Captioningcited by 1
  2. 2026TF-SSD: A Strong Pipeline via Synergic Mask Filter for Training-free Co-salient Object Detectioncited by 1
  3. 2026Rank-Consistent Set Reasoning for Co-Salient Object Detectioncited by 0