DeepVoting：一个用于部分遮挡下语义部分检测的鲁棒且可解释的深度网络
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
摘要：
在本文中，我们研究了检测对象的语义部分的任务，例如，汽车的一个轮子，处于部分遮挡状态。我们建议所有模型都应该在没有看到遮挡的情况下进行训练，同时能够转移学习到的知识来处理遮挡。此设置增加了收集指数级大数据集以覆盖遮挡模式的难度，并且更重要。在这种情况下，基于建议的深度网络，如RCNN系列，通常会产生令人不满意的结果，因为建议提取和分类阶段可能会被不相关的遮挡物所混淆。为了解决这个问题，[25]提出了一种投票机制，结合多个本地视觉线索来检测语义部分。即使由于遮挡而丢失了一些视觉线索，仍然可以检测到语义部分。然而，该方法是手动设计的，因此难以以端到端的方式进行优化。在本文中，我们提出了DeepVoting，它将[25]所示的鲁棒性融入到深度网络中，从而可以联合优化整个管道。具体来说，它在深度网络的中间特征之后添加了两层，例如，VGGNet的pool-4层。第一层提取局部视觉线索的证据，第二层利用视觉线索和语义部分之间的空间关系执行投票机制。我们还提出了一个改进的版本DeepVoting+，通过从对象外部的上下文中学习视觉线索。在实验中，DeepVoting在遮挡情况下的语义部分检测方面的性能明显优于包括Faster-RCNN在内的几种基线方法。此外，DeepVoting具有可解释性，因为可以通过查找投票线索来诊断检测结果。
英文摘要：
In this paper, we study the task of detecting semantic parts of an object, e.g., a wheel of a car, under partial occlusion. We propose that all models should be trained without seeing occlusions while being able to transfer the learned knowledge to deal with occlusions. This setting alleviates the difficulty in collecting an exponentially large dataset to cover occlusion patterns and is more essential. In this scenario, the proposal-based deep networks, like RCNN-series, often produce unsatisfactory results, because both the proposal extraction and classification stages may be confused by the irrelevant occluders. To address this, [25] proposed a voting mechanism that combines multiple local visual cues to detect semantic parts. The semantic parts can still be detected even though some visual cues are missing due to occlusions. However, this method is manually-designed, thus is hard to be optimized in an end-to-end manner. In this paper, we present DeepVoting, which incorporates the robustness shown by [25] into a deep network, so that the whole pipeline can be jointly optimized. Specifically, it adds two layers after the intermediate features of a deep network, e.g., the pool-4 layer of VGGNet. The first layer extracts the evidence of local visual cues, and the second layer performs a voting mechanism by utilizing the spatial relationship between visual cues and semantic parts. We also propose an improved version DeepVoting+ by learning visual cues from context outside objects. In experiments, DeepVoting achieves significantly better performance than several baseline methods, including Faster-RCNN, for semantic part detection under occlusion. In addition, DeepVoting enjoys explainability as the detection results can be diagnosed via looking up the voting cues.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
