mlsanityData to 7 Oct 2026

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

arXiv 2309.07753 ↗First posted 14 Sep 2023cs.CVcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 33 of its 48 references that are in the map. First posted on arXiv 14 Sep 2023.

Builds on

33 of 48 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 2.

6 papers.

  1. 2024Language-Driven Visual Consensus for Zero-Shot Semantic Segmentationcited by 6
  2. 2024Neural Assembler: Learning to Generate Fine-Grained Robotic Assembly Instructions from Multi-View Imagescited by 5
  3. 2025Visual Consensus Prompting for Co-Salient Object Detectioncited by 3
  4. 2024Self-supervised co-salient object detection via feature correspondence at multiple scalescited by 1
  5. 2026TF-SSD: A Strong Pipeline via Synergic Mask Filter for Training-free Co-salient Object Detectioncited by 1
  6. 2026Rank-Consistent Set Reasoning for Co-Salient Object Detectioncited by 0