Concealed Object Detection

By Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, Ling Shao. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

705 citations recorded by the source corpus.

We present the first systematic study on concealed object detection (COD), which aims to identify objects that are visually embedded in their background. The high intrinsic similarities between the concealed objects and their background make COD far more challenging than traditional object detection/segmentation. To better understand this task, we collect a large-scale dataset, called COD10K, which consists of 10,000 images covering concealed objects in diverse real-world scenarios from 78 object categories. Further, we provide rich annotations including object categories, object boundaries, challenging attributes, object-level labels, and instance-level annotations. Our COD10K is the largest COD dataset to date, with the richest annotations, which enables comprehensive concealed object understanding and can even be used to help progress several other vision tasks, such as detection, segmentation, classification etc. Motivated by how animals hunt in the wild, we also design a simple but strong baseline for COD, termed the Search Identification Network (SINet). Without any bells and whistles, SINet outperforms twelve cutting-edge baselines on all datasets tested, making them robust, general architectures that could serve as catalysts for future research in COD. Finally, we provide some interesting findings, and highlight several potential applications and future directions. To spark research in this new field, our code, dataset, and online demo are available at our project page: http://mmcheng.net/cod.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7c8ae2656dcdfdb6f26ed280828f153c4a025788

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Citations in the corpus

Structure-Measure: A New Way to Evaluate Foreground Maps (2017, background): “, RGB SOD [15,74,82,86], RGB-D SOD [23,26,73,84,85], RGBT SOD [80], light field SOD [36,60, 77], VSOD [33], 360 SOD, SID [41], Saliency Ranking [2], Co-SOD [22,24,79], and HR SOD [71]) community has begun to widely adopt this measure even in the camouflaged object detection [19,57,72,72] and medical”
Res2Net: A New Multi-Scale Backbone Architecture (2019, background): “segmentation [46], salient object detection [21], interactive image segmentation [41], video recognition [37], concealed object detection [18], and medical segmentation [19], [20], [66].”
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers (2021, background): “similar to surrounding tissues, with low contrast, providing them with powerful camouflage properties [11], [12], and making them difficult to identify; (3) Polycentric data.”
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions (2021, background): “, OCR [67, 65, 68], 3D [27, 10, 26] and medical [14, 15, 28] image analysis) to be explored in the future, and hope that PVT could serve as a good starting point.”
Simultaneously Localize, Segment and Rank the Camouflaged Objects (2021, methodology): “The training set includes 1,711 images from the COD10K-CAM training set [10] and 289 images are from the CAMO training set [22].”
Context-aware Cross-level Fusion Network for Camouflaged Object Detection (2021, methodology): “Following the training setting in [Fan et al., 2021a], for both CAMO and COD10K datasets, we use the default training sets.”
Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection (2022, background): “Recently, it has attracted ever-growing research interest from the computer vision community and facilitates many valuable real-life applications, such as search and rescue [6], species discovery [36], and medical image analysis [8, 9, 12, 18, 55].”
Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction (2023, methodology): “Performance-Params-FLOPs comparisons of some stateof-the-art deep learning-based COD methods on COD10K [4].”
Towards Large-Scale Small Object Detection: Survey and Benchmarks (2022, methodology): “As stated in [146], [147], the annotation quality of a dataset is the dominating factor for its lifespan as a benchmark, hence we install a two-step quality assurance procedure after the initial annotation.”