96 citations · 420 across the 20 of their papers we have counts for
18 papers · 1 filter
Improve Video Representation with Temporal Adversarial Augmentation
Jinhao Duan, Quanfu Fan, Hao Cheng +2
Recent works reveal that adversarial augmentation benefits the generalization of neural networks (NNs) if used in an appropriate manner. In this paper, we introduce Temporal Advers…
Grafting Vision Transformers
Jongwoo Park, Kumara Kahatapitiya, Donghyun Kim +3
Vision Transformers (ViTs) have recently become the state-of-the-art across many computer vision tasks. In contrast to convolutional networks (CNNs), ViTs enable global information…
Augmentation Learning for Semi-Supervised Classification
Tim Frommknecht, Pedro Alves Zipf, Quanfu Fan +2
Recently, a number of new Semi-Supervised Learning methods have emerged. As the accuracy for ImageNet and similar datasets increased over time, the performance on tasks beyond the…
Temporal Relevance Analysis for Video Action Models
Quanfu Fan, Donghyun Kim, Chun-Fu +4
In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature. We first propose a new approach to…
Can An Image Classifier Suffice For Action Recognition?
Quanfu Fan, Chun-Fu, Chen +1
We explore a new perspective on video understanding by casting the video recognition problem as an image recognition task. Our approach rearranges input video frames into super ima…
RegionViT: Regional-to-Local Attention for Vision Transformers
Chun-Fu Chen, Rameswar Panda, Quanfu Fan
Vision transformer (ViT) has recently shown its strong capability in achieving comparable results to convolutional neural networks (CNNs) on image classification. However, vanilla…
AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition
Rameswar Panda, Chun-Fu Chen, Quanfu Fan +4
Multi-modal learning, which focuses on utilizing various modalities to improve the performance of a model, is widely used in video recognition. While traditional multi-modal learni…
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
Chun-Fu Chen, Quanfu Fan, Rameswar Panda
The recently developed vision transformer (ViT) has achieved promising results on image classification compared to convolutional neural networks. Inspired by this, in this paper, w…
Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
Chun-Fu Chen, Rameswar Panda, Kandan Ramakrishnan +4
In recent years, a number of approaches based on 2D or 3D convolutional neural networks (CNN) have emerged for video action recognition, achieving state-of-the-art results on sever…
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…
Efficient Fusion of Sparse and Complementary Convolutions
Chun-Fu Chen, Quanfu Fan, Marco Pistoia +1
We propose a new method to create compact convolutional neural networks (CNNs) by exploiting sparse convolutions. Different from previous works that learn sparsity in models, we di…
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
Chun-Fu Chen, Quanfu Fan, Neil Mallinar +2
In this paper, we propose a novel Convolutional Neural Network (CNN) architecture for learning multi-scale feature representations with good tradeoffs between speed and accuracy. T…
Moments in Time Dataset: one million videos for event understanding
Mathew Monfort, Alex Andonian, Bolei Zhou +8
We present the Moments in Time Dataset, a large-scale human-annotated collection of one million short videos corresponding to dynamic events unfolding within three seconds. Modelin…
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Zhaowei Cai, Quanfu Fan, Rogerio S. Feris +1
A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detectio…