The following awards were given at ICCV 2015.
- PAMI Distinguished Researcher Award (1): Yann LeCun
- PAMI Distinguished Researcher Award (2): David Lowe
- PAMI Everingham Prize Winner (1): Andrea Vedaldi for VLFeat
- PAMI Everingham Prize Winner (2): Daniel Scharstein and Rick Szeliski for the Middlebury Datasets
- PAMI Helmholtz Prize (1): David Martin, Charles Fowlkes, Doron Tal, and Jitendra Malik for their ICCV 2001 paper "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics".
- PAMI Helmholtz Prize (2): Serge Belongie, Jitendra Malik, and Jan Puzicha, for their ICCV 2001 paper "Matching Shapes".
- Marr Prize: Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, and Samual Rota Bulo, for "Deep Neural Decision Forests".
- Marr Prize honorable mention: Saining Xie and Zhuowen Tu for "Holistically-Nested Edge Detection".
The above Marr prize winning papers are very nice, but here I also want to highlight three other papers I found interesting today.
By Ross Girshick.
Since 2014 the standard object detection pipeline for natural images is the R-CNN system which first extracts a set of object proposals then scores them using a convolutional neural network. The two key weaknesses of the approach are: first, the separation between proposal generation and scoring, preventing joint training of model parameters; and second the separate scoring of each hypothesis which leads to significant runtime overhead. This work and the follow-up work ("Faster R-CNNs" at NIPS this year) addresses both issues by proposing a joint model that is trained end-to-end, including proposal generation, leading to a new state of the art in object detection.
Unsupervised Visual Representation Learning by Context Prediction
By Carl Doersch, Abhinav Gupta, and Alexei A. Efros.
Supervised deep learning needs lots of labeled training data to achieve good performance. This paper investigates whether we can create and train deep neural networks on artificial tasks for which we can create large amounts of training data. In particular, the paper proposes to predict where a certain patch appears within the image. For this task, an almost infinite amount of training data is easily created. Perhaps surprisingly the resulting network, despite being trained on this artificial task, has learned useful representations for real vision tasks such as image classification.
Deep Fried Convnets
By Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alex Smola, Le Song, and Ziyu Wang.
In deep convolutional networks the last few densely connected layers have the most parameters and thus most of the required memory during test time and training. This work proposes to leverage the fastfood kernel approximation to replace densely connected layers with specific efficient and low parameter operations.
The empirical results are impressive and the fastfood justification is plausible, but I wonder if this work may even provide a hint at a more general approach to construct efficient neural network architectures by using arbitrary dense but efficient matrix operations (FFT, DCT, Walsh-Hadamard, etcetera).