Robust and Accurate Object Detection via Adversarial Learning
| Autor: | Boqing Gong, Li Zhang, Cihang Xie, Cho-Jui Hsieh, Mingxing Tan, Xiangning Chen |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Detector Computer Science - Computer Vision and Pattern Recognition Object (computer science) Object detection Image (mathematics) Machine Learning (cs.LG) Robustness (computer science) Distortion Classifier (linguistics) Benchmark (computing) Computer vision Artificial intelligence business |
| Zdroj: | CVPR |
| DOI: | 10.48550/arxiv.2103.13886 |
| Popis: | Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection. Noting that most state-of-the-art object detectors benefit from fine-tuning a pre-trained classifier, we first study how the classifiers' gains from various data augmentations transfer to object detection. The results are discouraging; the gains diminish after fine-tuning in terms of either accuracy or robustness. This work instead augments the fine-tuning stage for object detectors by exploring adversarial examples, which can be viewed as a model-dependent data augmentation. Our method dynamically selects the stronger adversarial images sourced from a detector's classification and localization branches and evolves with the detector to ensure the augmentation policy stays current and relevant. This model-dependent augmentation generalizes to different object detectors better than AutoAugment, a model-agnostic augmentation policy searched based on one particular detector. Our approach boosts the performance of state-of-the-art EfficientDets by +1.1 mAP on the COCO object detection benchmark. It also improves the detectors' robustness against natural distortions by +3.8 mAP and against domain shift by +1.3 mAP. Models are available at https://github.com/google/automl/tree/master/efficientdet/Det-AdvProp.md Comment: CVPR 2021. Models are available at https://github.com/google/automl/tree/master/efficientdet/Det-AdvProp.md |
| Databáze: | OpenAIRE |
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