172 citations · 1.2k across the 80 of their papers we have counts for
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'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems
Ravi Kiran Sarvadevabhatla, Shanthakumar Venkatraman, R. Venkatesh Babu
An examination of object recognition challenge leaderboards (ILSVRC, PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small differences amongst themselves…
Generalized Dropout
Suraj Srinivas, R. Venkatesh Babu
Deep Neural Networks often require good regularizers to generalize well. Dropout is one such regularizer that is widely used among Deep Learning practitioners. Recent work has show…
Training Sparse Neural Networks
Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu
Deep neural networks with lots of parameters are typically used for large-scale computer vision tasks such as image classification. This is a result of using dense matrix multiplic…
Compensating for Large In-Plane Rotations in Natural Images
Lokesh Boominathan, Suraj Srinivas, R. Venkatesh Babu
Rotation invariance has been studied in the computer vision community primarily in the context of small in-plane rotations. This is usually achieved by building invariant image fea…
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
Lokesh Boominathan, Srinivas S S Kruthiventi, R. Venkatesh Babu
Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds. We use a combination of deep and shallow, fully convolutio…
SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition
Ravi Kiran Sarvadevabhatla, Shiv Surya, Srinivas S S Kruthiventi +1
Current state of the art object recognition architectures achieve impressive performance but are typically specialized for a single depictive style (e.g. photos only, sketches only…
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Suraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri +3
Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep lea…