96 citations · 420 across the 20 of their papers we have counts for
5 papers · 1 filter
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation
Quanfu Fan, Chun-Fu Chen, Hilde Kuehne +2
Current state-of-the-art models for video action recognition are mostly based on expensive 3D ConvNets. This results in a need for large GPU clusters to train and evaluate such arc…
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding
Mathew Monfort, Bowen Pan, Kandan Ramakrishnan +7
Videos capture events that typically contain multiple sequential, and simultaneous, actions even in the span of only a few seconds. However, most large-scale datasets built to trai…
Adversarial T-shirt! Evading Person Detectors in A Physical World
Kaidi Xu, Gaoyuan Zhang, Sijia Liu +6
It is known that deep neural networks (DNNs) are vulnerable to adversarial attacks. The so-called physical adversarial examples deceive DNN-based decisionmakers by attaching advers…
Reasoning About Human-Object Interactions Through Dual Attention Networks
Tete Xiao, Quanfu Fan, Dan Gutfreund +3
Objects are entities we act upon, where the functionality of an object is determined by how we interact with it. In this work we propose a Dual Attention Network model which reason…
Interpreting Adversarial Examples by Activation Promotion and Suppression
Kaidi Xu, Sijia Liu, Gaoyuan Zhang +5
It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: images with imperceptible perturbations crafted to fool classifiers. However, i…