Sequentially Generated Instance-Dependent Image Representations for Classification
Authors: Matthieu Cord, patrick gallinari, Nicolas Thome, Ludovic Denoyer, Gabriel Dulac-Arnold
ICLR 2014

Abstract: In this paper, we investigate a new framework for image classification that adaptively generates spatial representations. Our strategy is based on a sequential process that learns to explore the different regions of any image in order to infer its category. In particular, the choice of regions is specific to each image, directed by the actual content of previously selected regions.The capacity of the system to handle incomplete image information as well as its adaptive region selection allow the system to perform well in budgeted classification tasks by exploiting a dynamicly generated representation of each image. We demonstrate the system's abilities in a series of image-based exploration and classification tasks that highlight its learned exploration and inference abilities.


Peer review:
[Review]:
This paper presents an approach that considers a sequence of local representations of an image, in order to classify it into one of many labels. The approach decomposes an image into multiple non-overlapping sub-windows, and tries to find a sequence of subsets of these sub-windows that can efficiently lead to classify the image. The idea is interesting as it could potentially classify faster by concentrating only on the relevant part of the image; on the other hand, the training complexity is significantly increased (and I suspect for the approach to work we should include many more sub-windows at various scales and potentially with overlaps). The proposed approach is not compared to any other approaches, for instance the work of Larochelle and Hinton, 2010. The results are encouraging but not groundbreaking: it seems one needs to see a significant portion of the image in order to get similar or better performance than the baseline, so it's not clear the proposal works that well. I wonder if the policy used to guide the search space among sub-windows could be analyzed better.