One of the main challenges in machine vision relates to the problem of obtaining robust
representation of visual features that remain unaffected by geometric transformations. This …

The significant power of deep learning networks has led to enormous development in object
detection. Over the last few years, object detector frameworks have achieved tremendous …

Regression-based methods for 3D human pose estimation directly predict the 3D pose
parameters from a 2D image using deep networks. While achieving state-of-the-art …

Can our video understanding systems perceive objects when a heavy occlusion exists in a
scene? To answer this question, we collect a large-scale dataset called OVIS for occluded …

Abstract While Visual Question Answering (VQA) has progressed rapidly, previous works
raise concerns about robustness of current VQA models. In this work, we study the …

The adversarial patch attack against image classification models aims to inject adversarially
crafted pixels within a restricted image region (ie, a patch) for inducing model …

Detecting partially occluded objects is a difficult task. Our experimental results show that
deep learning approaches, such as Faster R-CNN, are not robust at object detection under …

Computer vision systems in real-world applications need to be robust to partial occlusion
while also being explainable. In this work, we show that black-box deep convolutional …

In recent years, Generative Adversarial Networks have become ubiquitous in both research
and public perception, but how GANs convert an unstructured latent code to a high quality …

This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They
are at the heart of the enormous recent progress in artificial intelligence and are of growing …