In a classical paper [1] from 2001, Viola and Jones popularized the use of large rectangular image filters in order to obtain features for image recognition. The use of very large filters allowed Viola and Jones to compute features over very large receptive fields without blowing up the computation cost. For the next 10+ years, such features remained the staple of fast computer vision (e.g. [2]). The advent of deep learning made the use of integral-image features far less popular. Currently, state-of-the-art architectures invariably relying on very deep architectures. In these architectures sufficiently large receptive fields are obtained via the use of downsampling with subsequent upsampling [3] or via dilated convolutions [4]. All such tricks however have their downsides and usually necessitate the use of very deep networks.
The goal of this project is to implement an integral image-based filtering as a layer for deep architectures in Torch deep learning package, and to evaluate it for the task of learning very fast object detectors (as an alternative to e.g. [5]) and semantic segmentation systems (as an alternative to e.g. [3,4]). The hope is to obtain much shallower architectures, which at least for simple classes (e.g. roadsigns or upright pedestrians) will approach the performance of much deeper ones.
The project is supervised by Victor Lempitsky at Skoltech, Moscow, Russia.
[1] Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2 (2004): 137-154.
[2] Dollár, Piotr, Serge Belongie, and Pietro Perona. "The Fastest Pedestrian Detector in the West." BMVC. Vol. 2. No. 3. 2010.
[3] Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, 2015.
[4] Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions." arXiv preprint arXiv:1511.07122 (2015).[5] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed. SSD: Single shot multibox detector. ECCV 2016
[5] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed. SSD: Single shot multibox detector. ECCV 2016