Learning Data Augmentation Strategies for Object Detection
| Autor: | Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V. Le, Ekin D. Cubuk, Barret Zoph |
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| Rok vydání: | 2020 |
| Předmět: |
Network architecture
Theoretical computer science Computer science Inference 020207 software engineering 02 engineering and technology 010501 environmental sciences 01 natural sciences Object detection Learning data Transfer (computing) 0202 electrical engineering electronic engineering information engineering Value (mathematics) Transformation geometry 0105 earth and related environmental sciences |
| Zdroj: | Computer Vision – ECCV 2020 ISBN: 9783030585822 ECCV (27) |
| DOI: | 10.1007/978-3-030-58583-9_34 |
| Popis: | Much research on object detection focuses on building better model architectures and detection algorithms. Changing the model architecture, however, comes at the cost of adding more complexity to inference, making models slower. Data augmentation, on the other hand, doesn’t add any inference complexity, but is insufficiently studied in object detection for two reasons. First it is more difficult to design plausible augmentation strategies for object detection than for classification, because one must handle the complexity of bounding boxes if geometric transformations are applied. Secondly, data augmentation attracts less research attention perhaps because it is believed to add less value and to transfer poorly compared to advances in network architectures. |
| Databáze: | OpenAIRE |
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