16 citations · 32 across the 9 of their papers we have counts for
6 papers · 1 filter
Dissimilarity Coefficient based Weakly Supervised Object Detection
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
We consider the problem of weakly supervised object detection, where the training samples are annotated using only image-level labels that indicate the presence or absence of an ob…
Deep Frank-Wolfe For Neural Network Optimization
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of…
A Statistical Approach to Assessing Neural Network Robustness
Stefan Webb, Tom Rainforth, Yee Whye Teh +1
We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate th…
Learning Human Poses from Actions
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
We consider the task of learning to estimate human pose in still images. In order to avoid the high cost of full supervision, we propose to use a diverse data set, which consists o…
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan +5
Dense conditional random fields (CRFs) have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segm…
Smooth Loss Functions for Deep Top-k Classification
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
The top-k error is a common measure of performance in machine learning and computer vision. In practice, top-k classification is typically performed with deep neural networks train…