One of the challenges of computer vision is that it needs to adapt to color deviations in changeable environments. Therefore, minimizing the adverse effects of color deviation on the prediction is one of the main goals of vision task. Current solutions focus on using generative models to augment training data to enhance the invariance of input variation. However, such methods often introduce new noise, which limits the gain from generated data. To this end, this paper proposes a strategy eliminate deviation with deviation, which is named Random Color Dropout (RCD). Our hypothesis is that if there are color deviation between the query image and the gallery image, the retrieval results of some examples will be better after ignoring the color information. Specifically, this strategy balances the weights between color features and color-independent features in the neural network by dropouting partial color information in the training data, so as to overcome the effect of color devitaion. The proposed RCD can be combined with various existing ReID models without changing the learning strategy, and can be applied to other computer vision fields, such as object detection. Experiments on several ReID baselines and three common large-scale datasets such as Market1501, DukeMTMC, and MSMT17 have verified the effectiveness of this method. Experiments on Cross-domain tests have shown that this strategy is significant eliminating the domain gap. Furthermore, in order to understand the working mechanism of RCD, we analyzed the effectiveness of this strategy from the perspective of classification, which reveals that it may be better to utilize many instead of all of color information in visual tasks with strong domain variations.
翻译:计算机视觉的挑战之一是它需要适应可变环境中的颜色偏差。 因此, 将颜色偏差对预测的不利影响最小化是视觉任务的主要目标之一。 目前的解决办法侧重于使用基因模型来增加培训数据,以加强输入变异性。 然而, 这种方法往往会引入新的噪音, 从而限制从生成的数据中获得的收益。 为此, 本文提议了一个战略来消除偏差, 称为随机色落出( RCD) 。 我们的假设是, 如果查询图像和画廊图像之间出现颜色偏差, 一些例子的检索结果在忽略颜色信息之后会更好。 具体而言, 该战略通过在培训数据中丢弃部分颜色信息, 来平衡颜色特征和无色独立特征之间的重量, 以克服颜色变异效应的影响。 拟议的刚果民盟可以在不改变学习策略的情况下与现有的各种REID模型相结合, 并可以应用到其他计算机视觉领域, 如对象变异。 一些ReID基准的实验结果和三个通用的大规模数据设置, 在不考虑颜色信息的域域中, 杜克马德姆斯· 17 测试显示这个显著的域域域法 。