Optimistic and Pessimistic Neural Networks for Scene and Object Recognition
This work addresses uncertainty estimation for computer vision tasks, offering a test-time enhancement that is incremental to existing methods.
The paper tackled the problem of uncertainty modeling in convolutional neural networks by introducing a method to adjust predictions based on uncertainty, enabling optimistic or pessimistic scoring. It showed that this approach improves model performance at test time without additional training, with applications in object classification, detection, and scene attribute recognition.
In this paper the application of uncertainty modeling to convolutional neural networks is evaluated. A novel method for adjusting the network's predictions based on uncertainty information is introduced. This allows the network to be either optimistic or pessimistic in its prediction scores. The proposed method builds on the idea of applying dropout at test time and sampling a predictive mean and variance from the network's output. Besides the methodological aspects, implementation details allowing for a fast evaluation are presented. Furthermore, a multilabel network architecture is introduced that strongly benefits from the presented approach. In the evaluation it will be shown that modeling uncertainty allows for improving the performance of a given model purely at test time without any further training steps. The evaluation considers several applications in the field of computer vision, including object classification and detection as well as scene attribute recognition.