论文标题
人重新识别的渐进多阶段功能组合
Progressive Multi-stage Feature Mix for Person Re-Identification
论文作者
论文摘要
来自小地方地区的图像特征通常会在亲自重新识别任务中提供有力的证据。但是，美国有线电视新闻网（CNN）遭受了对最突出的地方区域的过多关注，因此忽略了其他歧视性线索，例如衣服上的头发，鞋子或徽标。 ％BDB建议在批处理中随机将一个块置于批处理，以扩大高响应区域。尽管BDB取得了显着的结果，但仍有改进的余地。在这项工作中，我们提出了一个渐进的多阶段特征混合网络（PMM），该网络使模型能够以渐进的方式找到更精确和多样的功能。具体来说，1。要强制模型寻找图像中的不同线索，我们采用了多阶段分类器，并期望该模型能够专注于每个阶段的互补区域。 2。我们提出了一个细心的特征硬混合（A-HARD-MIX），以通过当前批次中的负面示例替换出显着的特征块，其标签与当前样本不同。 3。在REID数据集（例如Market-1501，Dukemtmc-Reid和Cuhk03）上进行了广泛的实验，表明所提出的方法可以显着提高重新识别性能。
Image features from a small local region often give strong evidence in person re-identification task. However, CNN suffers from paying too much attention on the most salient local areas, thus ignoring other discriminative clues, e.g., hair, shoes or logos on clothes. %BDB proposes to randomly drop one block in a batch to enlarge the high response areas. Although BDB has achieved remarkable results, there still room for improvement. In this work, we propose a Progressive Multi-stage feature Mix network (PMM), which enables the model to find out the more precise and diverse features in a progressive manner. Specifically, 1. to enforce the model to look for different clues in the image, we adopt a multi-stage classifier and expect that the model is able to focus on a complementary region in each stage. 2. we propose an Attentive feature Hard-Mix (A-Hard-Mix) to replace the salient feature blocks by the negative example in the current batch, whose label is different from the current sample. 3. extensive experiments have been carried out on reID datasets such as the Market-1501, DukeMTMC-reID and CUHK03, showing that the proposed method can boost the re-identification performance significantly.
