Are Sampling Heuristics Necessary in Object Detectors?
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Summary
It is revealed that, by decoupling objectness estimation from classification to transfer the imbalance, the sampling heuristics could be abandoned in object detectors with equivalent performance than their vanilla models.
- Type
- article
- Published
- 2019-09-11
- Cited by
- 0
- References
- 54
- Access
- Open access
- OpenAlex
- https://openalex.org/W2972571929
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:202565683
Keywords
Computer science, Sampling (signal processing), Heuristics, Artificial intelligence, Pascal (unit)
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- You Only Look Once: Unified, Real-Time Object Detection
- Cascade object detection with deformable part models
- Selective Search for Object Recognition
- RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
- A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown
- DenseBox: Unifying Landmark Localization with End to End Object Detection
- Robust Real-Time Face Detection
- SMOTE: Synthetic Minority Over-sampling Technique
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition
- Training Region-Based Object Detectors with Online Hard Example Mining
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- Aggregated Residual Transformations for Deep Neural Networks
- Feature Pyramid Networks for Object Detection
- YOLO9000: Better, Faster, Stronger
- Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
- RON: Reverse Connection with Objectness Prior Networks for Object Detection
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