论文标题
用双任务交互式变压器增强伪装的对象检测
Boosting Camouflaged Object Detection with Dual-Task Interactive Transformer
论文作者
论文摘要
伪装的物体检测打算发现隐藏在周围环境中的隐藏物体。现有方法遵循以生物启发的框架，该框架首先定位对象，第二个完善边界。我们认为，伪装对象的发现取决于对对象和边界的反复搜索。经常性处理使人类疲倦和无助，但这只是具有全球搜索能力的变压器的优势。因此，提出了双任务交互式变压器来检测伪装对象的准确位置及其详细的边界。边界特征被认为是改进伪装对象检测的查询，同时，对象特征被视为改善边界检测的查询。伪装的对象检测和边界检测通过多头自我注意完全相互作用。此外，为了获得初始对象功能和边界特征，采用基于变压器的骨干来提取前景和背景。前景只是对象，而前景负背景被认为是边界。在这里，边界特征可以从前景和背景的模糊边界区域获得。在对象，背景和边界地面真理的监督下，提出的模型在公共数据集中实现了最新的性能。 https://github.com/liuzywen/cod
Camouflaged object detection intends to discover the concealed objects hidden in the surroundings. Existing methods follow the bio-inspired framework, which first locates the object and second refines the boundary. We argue that the discovery of camouflaged objects depends on the recurrent search for the object and the boundary. The recurrent processing makes the human tired and helpless, but it is just the advantage of the transformer with global search ability. Therefore, a dual-task interactive transformer is proposed to detect both accurate position of the camouflaged object and its detailed boundary. The boundary feature is considered as Query to improve the camouflaged object detection, and meanwhile the object feature is considered as Query to improve the boundary detection. The camouflaged object detection and the boundary detection are fully interacted by multi-head self-attention. Besides, to obtain the initial object feature and boundary feature, transformer-based backbones are adopted to extract the foreground and background. The foreground is just object, while foreground minus background is considered as boundary. Here, the boundary feature can be obtained from blurry boundary region of the foreground and background. Supervised by the object, the background and the boundary ground truth, the proposed model achieves state-of-the-art performance in public datasets. https://github.com/liuzywen/COD
