Rank-Consistent Set Reasoning for Co-Salient Object Detection

• URL: http://arxiv.org/abs/2609.13706v1
• Date: Sat, 12 Sep 2026 04:34:48 GMT
• Title: Rank-Consistent Set Reasoning for Co-Salient Object Detection
• Abstract summary: Co-salient object detection (Co-SOD) requires a model to find foreground regions that are salient in individual images and supported by the image group.<n>We present emphRank-Consistent Set Reasoning (RCSR), a supervised dense-prediction framework that models a group as an unordered set.
• Score: 0.548014499873554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Co-salient object detection (Co-SOD) requires a model to find foreground regions that are salient in individual images and supported by the image group. We present \emph{Rank-Consistent Set Reasoning} (RCSR), a supervised dense-prediction framework that models a group as an unordered set rather than as a sequence of images or a semantic label. The core idea is to rank how strongly each spatial region agrees with a small collection of learned group slots at every image scale, and to aggregate these ranks with a robust trimmed statistic. This suppresses accidental pairwise matches and prevents one atypical group member from dominating the shared representation. A set encoder builds group slots directly from multi-scale visual features, while a rank-consistency gate measures whether the ordering of candidate regions is stable across group members. The gated slots are decoded jointly with per-image features to produce co-saliency maps. The model contains no natural-language branch, no open-vocabulary detector, and no external segmentation model. We further introduce a group permutation objective and hard-distractor augmentation so that the model learns the properties of a set-level target rather than memorizing image order or isolated visual saliency. We formulate an evaluation protocol for CoCA, CoSal2015, and CoSOD3k, together with tests of group-size robustness, distractor rejection, order invariance, and cross-dataset transfer.

Related papers

• Weaving Visual Narratives: Agentic Image Bundle Composition Beyond Atomic Visual Matching [39.681024131939814]
  We introduce **Image Bundle Composition (IBC)**, a novel paradigm that shifts the objective from ranking individual images to dynamically composing cohesive image bundles.<n>IBC presents a severe explosion challenge and demands modeling non-decomposable joint relevance.<n>By employing a Large Language Model to adaptively search for missing relational roles and utilizing a Vision-Language Model for whole-bundle verification, BundleWeaver effectively navigates the space.
  arXiv  Detail & Related papers  (2026-08-27T07:50:11Z)
• OccluRank: Controllable Occlusion-Aware Layout-to-Image Generation by Adding Just an Ordinal Rank [57.48234285746084]
  OccluRank is a layout-to-image framework that augments each bounding box with only one ordinal rank.<n>Experiments show that OccluRank more reliably preserves target instances, follows specified layouts, and realizes desired attribute relationships.
  arXiv  Detail & Related papers  (2026-08-21T09:53:06Z)
• Grounding Everything: Emerging Localization Properties in Vision-Language Transformers [51.260510447308306]
  We show that pretrained vision-language (VL) models allow for zero-shot open-vocabulary object localization without any fine-tuning. We propose a Grounding Everything Module (GEM) that generalizes the idea of value-value attention introduced by CLIPSurgery to a self-self attention path. We evaluate the proposed GEM framework on various benchmark tasks and datasets for semantic segmentation.
  arXiv  Detail & Related papers  (2023-12-01T19:06:12Z)
• Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-Masking [23.60044777118441]
  We introduce a group selective exchange-masking (GSEM) approach for enhancing the robustness of the CoSOD model. GSEM selects a subset of images from each group using a novel learning-based strategy, then the selected images are exchanged. To simultaneously consider the uncertainty introduced by irrelevant images and the consensus features of the remaining relevant images in the group, we designed a latent variable generator branch and CoSOD transformer branch.
  arXiv  Detail & Related papers  (2023-10-16T10:40:40Z)
• Co-Salient Object Detection with Semantic-Level Consensus Extraction and Dispersion [27.120768849942145]
  Co-salient object detection aims to highlight the common salient object in each image. We propose a hierarchical Transformer module for extracting semantic-level consensus. A Transformer-based dispersion module takes into account the variation of the co-salient object in different scenes.
  arXiv  Detail & Related papers  (2023-09-14T14:39:07Z)
• Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation [75.32213865436442]
  We propose an end-to-end multi-granularity denoising and bidirectional alignment (MDBA) model to alleviate the noisy label and multi-class generalization issues. The MDBA model can reach the mIoU of 69.5% and 70.2% on validation and test sets for the PASCAL VOC 2012 dataset.
  arXiv  Detail & Related papers  (2023-05-09T03:33:43Z)
• Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
  This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
  arXiv  Detail & Related papers  (2020-12-09T12:40:13Z)
• CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection [91.91911418421086]
  Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue is how to effectively capture co-saliency cues by modeling and exploiting inter-image relationships. We present an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images.
  arXiv  Detail & Related papers  (2020-11-10T04:28:11Z)
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification [84.43394420267794]
  We propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. Our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.
  arXiv  Detail & Related papers  (2020-03-18T12:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.