2 citations · 2 across the 3 of their papers we have counts for
7 papers
Temporally-Transferable Perturbations: Efficient, One-Shot Adversarial Attacks for Online Visual Object Trackers
Krishna Kanth Nakka, Mathieu Salzmann
In recent years, the trackers based on Siamese networks have emerged as highly effective and efficient for visual object tracking (VOT). While these methods were shown to be vulner…
Towards Robust Fine-grained Recognition by Maximal Separation of Discriminative Features
Krishna Kanth Nakka, Mathieu Salzmann
Adversarial attacks have been widely studied for general classification tasks, but remain unexplored in the context of fine-grained recognition, where the inter-class similarities…
Indirect Local Attacks for Context-aware Semantic Segmentation Networks
Krishna Kanth Nakka, Mathieu Salzmann
Recently, deep networks have achieved impressive semantic segmentation performance, in particular thanks to their use of larger contextual information. In this paper, we show that…
Detecting the Unexpected via Image Resynthesis
Krzysztof Lis, Krishna Nakka, Pascal Fua +1
Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we ta…
Interpretable BoW Networks for Adversarial Example Detection
Krishna Kanth Nakka, Mathieu Salzmann
The standard approach to providing interpretability to deep convolutional neural networks (CNNs) consists of visualizing either their feature maps, or the image regions that contri…
My camera can see through fences: A deep learning approach for image de-fencing
Sankaraganesh Jonna, Krishna Kanth Nakka, Rajiv R. Sahay
In recent times, the availability of inexpensive image capturing devices such as smartphones/tablets has led to an exponential increase in the number of images/videos captured. How…