Referring Camouflaged Object Detection
Xuying Zhang, Bowen Yin, Zheng Lin, Qibin Hou, Deng-Ping Fan,, Ming-Ming Cheng

TL;DR
This paper introduces the new task of referring camouflaged object detection, proposing a large dataset and a dual-branch framework that effectively segments specified camouflaged objects based on referring images.
Contribution
The paper presents the first large-scale dataset R2C7K and a novel dual-branch R2CNet framework for referring camouflaged object detection, advancing the ability to identify specific camouflaged targets.
Findings
R2CNet outperforms existing COD methods in segmenting specified camouflaged objects.
The dataset R2C7K covers 64 object categories in real-world scenarios.
The proposed modules improve the accuracy of identifying target objects.
Abstract
We consider the problem of referring camouflaged object detection (Ref-COD), a new task that aims to segment specified camouflaged objects based on a small set of referring images with salient target objects. We first assemble a large-scale dataset, called R2C7K, which consists of 7K images covering 64 object categories in real-world scenarios. Then, we develop a simple but strong dual-branch framework, dubbed R2CNet, with a reference branch embedding the common representations of target objects from referring images and a segmentation branch identifying and segmenting camouflaged objects under the guidance of the common representations. In particular, we design a Referring Mask Generation module to generate pixel-level prior mask and a Referring Feature Enrichment module to enhance the capability of identifying specified camouflaged objects. Extensive experiments show the superiority…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMultimodal Machine Learning Applications · Visual Attention and Saliency Detection · Ocular Oncology and Treatments
