Projects (New)

Unsupervised Discovery of Mid-Level Discriminative Patches
Saurabh Singh, Abhinav Gupta, Alexei A. Efros
Accepted in ECCV 2012

The goal of this paper is to discover a set of discriminative patches which can serve as a fully unsupervised mid-level visual representation. We pose this as an unsupervised discriminative clustering problem on a huge dataset of image patches. We use an iterative procedure which alternates between clustering and training discriminative classifiers, while applying careful cross-validation at each step to prevent overfitting.

What Makes Paris Look like Paris?
Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, Alexei A. Efros
ACM Transactions on Graphics (SIGGRAPH 2012).

Given a large repository of geotagged imagery, the goal is to automatically find visual elements, e.g. windows, balconies, and street signs, that are most distinctive for a certain geo-spatial area, for example the city of Paris.

Constrained Semi-Supervised Learning using Attributes and Comparative Attributes.
Abhinav Shrivastava, Saurabh Singh, Abhinav Gupta
Accepted in ECCV 2012 (Oral)

Existing semi-supervised approaches are typically unreliable and face semantic drift because the learning task is under-constrained. This is primarily because they ignore the strong interactions that often exist between categories, such as the common attributes shared across categories as well as the attributes which make one different from another. The goal of this paper is to exploit these relationships and constrain the semi-supervised learning problem leading to better learned classifiers.

Here is a list of some undergrad projects and masters projects that I did.