DOI: CVPR 2016
Key Qustion
- the training set is distinguished by a large imbalance between the number of annotated objects and the number of background examples
Contribution
- Make training more effective and efficient.
- OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use.
- The candidate examples are subsampled according to a distribution that favors diverse, high loss instances.
- It removes the need for several heuristics and hyperparameters commonly used in region-based ConvNets.
- It yields a consistent and significant boosts in mean average precision
- Its effectiveness increases as the training set becomes larger and more difficult, as demonstrated by results on the MS COCO dataset
Architecture
Fast R-CNN
OHEM
Experiments
Conclusion
- OHEM eliminates several heuristics and hyperparameters in common use by automatically selecting hard examples, thus simplifying training.
- Though we used Fast R-CNN throughout this paper, OHEM can be used for training any region-based ConvNet detector.
Unknown Key Words
- bootstrapping = hard negative mining rely on aforementioned alternation template:(a) for some period of time a fixed model is used to find new examples to add to the active training set; (b) then, for some period of time the model is trained on the fixed active training set.
- hard positive example = false positive example
Questions
- However, there is a small caveat: co-located RoIs with high overlap are likely to have correlated losses.
- use standard non-maximum suppression (NMS) to perform deduplication
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Self-Learning
- SGD is not suitable for bootstrapping template
- 2 methods of hard example mining
- remove easy example and then add some hard example
- add false positives to dataset to train the model again
- proposal’s IOU with ground truth is in the interval [bg_lo, 0.5), bg_lo = 0.1 is helpful but ignore some infrequent, but import, difficult background regions.
- OHEM is robust in case one needs fewer images per batch in order to reduce GPU memory usage.