Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Khan, Salman H."'
Visual Question Answering (VQA) has emerged as a Visual Turing Test to validate the reasoning ability of AI agents. The pivot to existing VQA models is the joint embedding that is learned by combining the visual features from an image and the semanti
Externí odkaz:
http://arxiv.org/abs/2001.07059
In the last two decades, luggage scanning has globally become one of the prime aviation security concerns. Manual screening of the baggage items is a cumbersome, subjective and inefficient process. Hence, many researchers have developed Xray imagery-
Externí odkaz:
http://arxiv.org/abs/1912.04251
Visual Question Answering (VQA) models employ attention mechanisms to discover image locations that are most relevant for answering a specific question. For this purpose, several multimodal fusion strategies have been proposed, ranging from relativel
Externí odkaz:
http://arxiv.org/abs/1908.03289
3D shape generation is a challenging problem due to the high-dimensional output space and complex part configurations of real-world objects. As a result, existing algorithms experience difficulties in accurate generative modeling of 3D shapes. Here,
Externí odkaz:
http://arxiv.org/abs/1906.03650
Adversarial examples reveal the blind spots of deep neural networks (DNNs) and represent a major concern for security-critical applications. The transferability of adversarial examples makes real-world attacks possible in black-box settings, where th
Externí odkaz:
http://arxiv.org/abs/1905.11736
Convolutional Neural Networks have achieved significant success across multiple computer vision tasks. However, they are vulnerable to carefully crafted, human-imperceptible adversarial noise patterns which constrain their deployment in critical secu
Externí odkaz:
http://arxiv.org/abs/1901.01677
Current Visual Question Answering (VQA) systems can answer intelligent questions about `Known' visual content. However, their performance drops significantly when questions about visually and linguistically `Unknown' concepts are presented during inf
Externí odkaz:
http://arxiv.org/abs/1811.12772
Deep neural networks (DNNs) can be easily fooled by adding human imperceptible perturbations to the images. These perturbed images are known as `adversarial examples' and pose a serious threat to security and safety critical systems. A litmus test fo
Externí odkaz:
http://arxiv.org/abs/1811.09020
Deep neural networks (DNNs) have shown vulnerability to adversarial attacks, i.e., carefully perturbed inputs designed to mislead the network at inference time. Recently introduced localized attacks, Localized and Visible Adversarial Noise (LaVAN) an
Externí odkaz:
http://arxiv.org/abs/1807.01216
Autor:
Farazi, Moshiur R, Khan, Salman H
Publikováno v:
Proceedings of the British Machine Vision Conference (250) 2018
Existing attention mechanisms either attend to local image grid or object level features for Visual Question Answering (VQA). Motivated by the observation that questions can relate to both object instances and their parts, we propose a novel attentio
Externí odkaz:
http://arxiv.org/abs/1805.04247