Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection

By Fan Yang, Qiang Zhai, Xin Li, Rui Huang, Deng-Ping Fan. IEEE International Conference on Computer Vision, 2021

309 citations recorded by the source corpus.

Spotting objects that are visually adapted to their surroundings is challenging for both humans and AI. Conventional generic / salient object detection techniques are suboptimal for this task because they tend to only discover easy and clear objects, while overlooking the difficult-to-detect ones with inherent uncertainties derived from indistinguishable textures. In this work, we contribute a novel approach using a probabilistic representational model in combination with transformers to explicitly reason under uncertainties, namely uncertainty-guided transformer reasoning (UGTR), for camouflaged object detection. The core idea is to first learn a conditional distribution over the backbone's output to obtain initial estimates and associated uncertainties, and then reason over these uncertain regions with attention mechanism to produce final predictions. Our approach combines the benefits of both Bayesian learning and Transformer-based reasoning, allowing the model to handle camouflaged object detection by leveraging both deterministic and probabilistic information. We empirically demonstrate that our proposed approach can achieve higher accuracy than existing state-of-the-art models on CHAMELEON, CAMO and COD10K datasets. Code is available at https://github.com/fanyang587/UGTR.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c0098a949d3f78ef4c21cbf9c6d1adf9c4412e3b

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In Computer Vision and Pattern Recognition

Citations in the corpus

Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection (2022, methodology): “Some uncertainty-aware methods [20, 47] are proposed to model and cope with the uncertainty in data annotation or COD data itself.”