论文标题
与前景检测和周期一致性的深度语义匹配
Deep Semantic Matching with Foreground Detection and Cycle-Consistency
论文作者
论文摘要
由于背景混乱，大量尺度和姿势差异以及较大的类内变化，建立对象实例之间建立密集的语义对应仍然是一个具有挑战性的问题。在本文中，我们基于深层网络解决了弱监督的语义匹配，其中仅提供没有手动关键点对应注释的图像对。为了通过这种较弱的监督形式促进网络培训，我们1）明确估计前景区域以抑制背景混乱的影响，2）产生循环一致的损失，以实施跨多个图像的预测转换，以使几何图像具有几何形式，并且是一致的。我们使用PF-Pascal数据集训练提出的模型，并评估PF-Pascal，PF-Willow和TSS数据集的性能。广泛的实验结果表明，所提出的方法对最先进的方法表现出色。
Establishing dense semantic correspondences between object instances remains a challenging problem due to background clutter, significant scale and pose differences, and large intra-class variations. In this paper, we address weakly supervised semantic matching based on a deep network where only image pairs without manual keypoint correspondence annotations are provided. To facilitate network training with this weaker form of supervision, we 1) explicitly estimate the foreground regions to suppress the effect of background clutter and 2) develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent. We train the proposed model using the PF-PASCAL dataset and evaluate the performance on the PF-PASCAL, PF-WILLOW, and TSS datasets. Extensive experimental results show that the proposed approach performs favorably against the state-of-the-art methods.
