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ILSVRC2014检测总结
ILSVRC 2014结束一段时间了。从下面的表格来看,基本都是RCNN的路子,但是这些牛队都做了改进。自己和人家比差的太远啊,努力。
team | results | Spotlights and improve |
GoogLeNet | 0.439329(6 m) 0.38(1m) | Rcnn 1. Increase size of super-pixels by 2X 2. Add multibox* proposals |
CUHK DeepID-Net | 0.406659 | RCNN + Bounding box rejection using def-pooling layer 1000 object-level annotation 200 object-level annotation |
Deep Insight | 0.404517 | Original RCNN + 9conv + SPM + more iterations + Structural Edge Proposal + 7/8/9 Conv Ensemble + CLS Context |
NUS | 0.37212 | Rcnn framework, with nin in cnn |
UvA-Euvision | 0.354213(aug) 0.32.253(prov) | Selective search + cnn |
MSRA Visual Computing | 0.351103 | A combination of multiple SPP-net-based models (no outside data) |
Berkeley Vision | 0.345213 | R-CNN baseline |
ILSVRC2014检测总结