Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method

21 Jan 2021  ·  Yunpeng Gong, Liqing Huang, Lifei Chen ·

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.

PDF Abstract

Results from the Paper


Ranked #2 on Person Re-Identification on Market-1501 (using extra training data)

     Get a GitHub badge
Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Person Re-Identification DukeMTMC-reID RGT&RGPR (RK) Rank-1 94.3 # 3
mAP 92.7 # 5
Person Re-Identification Market-1501 RGT&RGPR (RK) Rank-1 96.9 # 5
mAP 95.6 # 2
Person Re-Identification MSMT17 RGT&RGPR(without RK) Rank-1 86.2 # 14
mAP 65.9 # 20

Methods