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 tackles the problem of deciding where to look on an image. The proposed solution is to start from a center region and use the information extracted to decide what region to examine next. This is trained through reinforcement learning.

The paper is well organized and clear. Experiments on 2 benchmarks (15 scenes and people playing musical instruments) show that selecting a smaller number of subregions of the image does not result in a big loss of accuracy, or even improves accuracy by eliminating noise from information-poor regions.

Figures 5 and 6 have unreadable annotations, this should be fixed and/or the caption under the figure fleshed out to better describe the results.

The datasets are limited and this would need to be extended to more realistic datasets, but the problem tackled is important and the proposed solution is a welcome step in this direction.