Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection

• URL: http://arxiv.org/abs/2412.16840v1
• Date: Sun, 22 Dec 2024 03:25:43 GMT
• Title: Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection
• Authors: Yi Liu, Chengxin Li, Xiaohui Dong, Lei Li, Dingwen Zhang, Shoukun Xu, Jungong Han,
• Abstract summary: We propose a task-agnostic framework to unify Salient Object Detection (SOD) and Camouflaged Object Detection (COD) We design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps. Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings.
• Score: 73.85890512959861
• License:
• Abstract: Achieving joint learning of Salient Object Detection (SOD) and Camouflaged Object Detection (COD) is extremely challenging due to their distinct object characteristics, i.e., saliency and camouflage. The only preliminary research treats them as two contradictory tasks, training models on large-scale labeled data alternately for each task and assessing them independently. However, such task-specific mechanisms fail to meet real-world demands for addressing unknown tasks effectively. To address this issue, in this paper, we pioneer a task-agnostic framework to unify SOD and COD. To this end, inspired by the agreeable nature of binary segmentation for SOD and COD, we propose a Contrastive Distillation Paradigm (CDP) to distil the foreground from the background, facilitating the identification of salient and camouflaged objects amidst their surroundings. To probe into the contribution of our CDP, we design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps. Besides the supervised setting, our CDP can be seamlessly integrated into unsupervised settings, eliminating the reliance on extensive human annotations. Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings, compared with existing state-of-the-art approaches. Code is available on https://github.com/liuyi1989/Seamless-Detection.

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