Royer, Amélie; Chatterjee, KrishnenduIST Austria
Classifiers for object categorization are usually evaluated by their accuracy on a set of i.i.d. test examples. This provides us with an estimate of the expected error when applying the classifiers to a single new image. In real application, however, classifiers are rarely only used for a single image and then discarded. Instead, they are applied sequentially to many images, and these are typically not i.i.d. samples from a fixed data distribution, but they carry dependencies and their class distribution varies over time. In this work, we argue that the phenomenon of correlated data at prediction time is not a nuisance, but a blessing in disguise. We describe a probabilistic method for adapting classifiers at prediction time without having to retrain them. We also introduce a framework for creating realistically distributed image sequences, which offers a way to benchmark classifier adaptation methods, such as the one we propose. Experiments on the ILSVRC2010 and ILSVRC2012 datasets show that adapting object classification systems at prediction time can significantly reduce their error rate, even with no additional human feedback.
1401 - 1409
CVPR: Computer Vision and Pattern Recognition
Boston, MA, United States
2015-06-07 – 2015-06-12
Royer A, Lampert C. Classifier adaptation at prediction time. In: IEEE; 2015:1401-1409. doi:10.1109/CVPR.2015.7298746
Royer, A., & Lampert, C. (2015). Classifier adaptation at prediction time (pp. 1401–1409). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298746
Royer, Amélie, and Christoph Lampert. “Classifier Adaptation at Prediction Time,” 1401–9. IEEE, 2015. https://doi.org/10.1109/CVPR.2015.7298746.
A. Royer and C. Lampert, “Classifier adaptation at prediction time,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 1401–1409.
Royer A, Lampert C. 2015. Classifier adaptation at prediction time. CVPR: Computer Vision and Pattern Recognition, 1401–1409.
Royer, Amélie, and Christoph Lampert. Classifier Adaptation at Prediction Time. IEEE, 2015, pp. 1401–09, doi:10.1109/CVPR.2015.7298746.
All files available under the following license(s):
This Item is protected by copyright and/or related rights. [...]
Link(s) to Main File(s)