Discriminative Latent Variable Models for Object Detection
Abstract
In this talk, I will discuss recent work by colleagues and myself on discriminative latent-variable models for object detection. Object recognition is one of the fundamental challenges of computer vision. We specifically consider the task of localizing and detecting instances of a generic object category, such as people or cars, in cluttered real-word images. Recent benchmark competitions such as the PASCAL Visual Object Challenge suggest our method is the state-of-the-art system for such tasks. This success, combined with publically-available code that runs orders of magnitude faster than comparable approaches, has turned our system into a standard baseline for contemporary research on object recognition.
Cite
Text
Felzenszwalb et al. "Discriminative Latent Variable Models for Object Detection." International Conference on Machine Learning, 2010.Markdown
[Felzenszwalb et al. "Discriminative Latent Variable Models for Object Detection." International Conference on Machine Learning, 2010.](https://mlanthology.org/icml/2010/felzenszwalb2010icml-discriminative/)BibTeX
@inproceedings{felzenszwalb2010icml-discriminative,
title = {{Discriminative Latent Variable Models for Object Detection}},
author = {Felzenszwalb, Pedro F. and Girshick, Ross B. and McAllester, David A. and Ramanan, Deva},
booktitle = {International Conference on Machine Learning},
year = {2010},
pages = {11-12},
url = {https://mlanthology.org/icml/2010/felzenszwalb2010icml-discriminative/}
}