Out-of-Distribution Detection for Adaptive Computer Vision
It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions. This paper proposes a method to adapt camera parameters according to a normalizing flow-based out-of-distibution detector. A small-scale study is conducted which shows that adapting camera parameters according to this out-of-distibution detector leads to an average increase of 3 to 4 percentage points in mAP, mAR and F1 performance metrics of a YOLOv4 object detector. As a secondary result, this paper also shows that it is possible to train a normalizing flow model for out-of-distribution detection on the COCO dataset, which is larger and more diverse than most benchmarks for out-of-distibution detectors.
PDF Abstract
Datasets
MS COCO
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Bottom-up Path Augmentation •
Convolution •
Cosine Annealing •
CSPDarknet53 •
CutMix •
Darknet-53 •
DropBlock •
FPN •
Global Average Pooling •
Grid Sensitive •
k-Means Clustering •
Label Smoothing •
Logistic Regression •
Max Pooling •
Mish •
PAFPN •
ReLU •
Residual Connection •
Sigmoid Activation •
Softmax •
Softplus •
Spatial Attention Module •
Spatial Pyramid Pooling •
Tanh Activation •
YOLOv3 •
YOLOv4
