Search Results for author: Quanfu Fan

Found 26 papers, 14 papers with code

Random Laplace Feature Maps for Semigroup Kernels on Histograms

no code implementations CVPR 2014 Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney

With the goal of accelerating the training and testing complexity of nonlinear kernel methods, several recent papers have proposed explicit embeddings of the input data into low-dimensional feature spaces, where fast linear methods can instead be used to generate approximate solutions.

Event Detection Image Classification

Temporal Sequence Modeling for Video Event Detection

no code implementations CVPR 2014 Yu Cheng, Quanfu Fan, Sharath Pankanti, Alok Choudhary

Based on this idea, we represent a video by a sequence of visual words learnt from the video, and apply the Sequence Memoizer [21] to capture long-range dependencies in a temporal context in the visual sequence.

Event Detection General Classification

Sparse-Complementary Convolution for Efficient Model Utilization on CNNs

no code implementations ICLR 2018 Chun-Fu (Richard) Chen, Jinwook Oh, Quanfu Fan, Marco Pistoia, Gwo Giun (Chris) Lee

By simply replacing the convolution of a CNN with our sparse-complementary convolution, at the same FLOPs and parameters, we can improve top-1 accuracy on ImageNet by 0. 33% and 0. 18% for ResNet-101 and ResNet-152, respectively.

Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition

3 code implementations ICLR 2019 Chun-Fu Chen, Quanfu Fan, Neil Mallinar, Tom Sercu, Rogerio Feris

The proposed approach demonstrates improvement of model efficiency and performance on both object recognition and speech recognition tasks, using popular architectures including ResNet and ResNeXt.

Object Object Recognition +2

Structured Adversarial Attack: Towards General Implementation and Better Interpretability

1 code implementation ICLR 2019 Kaidi Xu, Sijia Liu, Pu Zhao, Pin-Yu Chen, huan zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin

When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and adversarial example.

Adversarial Attack

Efficient Fusion of Sparse and Complementary Convolutions

no code implementations7 Aug 2018 Chun-Fu Chen, Quanfu Fan, Marco Pistoia, Gwo Giun Lee

We propose a new method to create compact convolutional neural networks (CNNs) by exploiting sparse convolutions.

General Classification Object +2

Interpreting Adversarial Examples by Activation Promotion and Suppression

no code implementations3 Apr 2019 Kaidi Xu, Sijia Liu, Gaoyuan Zhang, Mengshu Sun, Pu Zhao, Quanfu Fan, Chuang Gan, Xue Lin

It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: images with imperceptible perturbations crafted to fool classifiers.

Adversarial Robustness

Reasoning About Human-Object Interactions Through Dual Attention Networks

no code implementations ICCV 2019 Tete Xiao, Quanfu Fan, Dan Gutfreund, Mathew Monfort, Aude Oliva, Bolei Zhou

The model not only finds when an action is happening and which object is being manipulated, but also identifies which part of the object is being interacted with.

Human-Object Interaction Detection Object

Adversarial T-shirt! Evading Person Detectors in A Physical World

1 code implementation ECCV 2020 Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, Xue Lin

To the best of our knowledge, this is the first work that models the effect of deformation for designing physical adversarial examples with respect to-rigid objects such as T-shirts.

Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition

1 code implementation CVPR 2021 Chun-Fu Chen, Rameswar Panda, Kandan Ramakrishnan, Rogerio Feris, John Cohn, Aude Oliva, Quanfu Fan

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 several large-scale benchmark datasets.

Action Recognition Temporal Action Localization

CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

14 code implementations ICCV 2021 Chun-Fu Chen, Quanfu Fan, Rameswar Panda

To this end, we propose a dual-branch transformer to combine image patches (i. e., tokens in a transformer) of different sizes to produce stronger image features.

General Classification Image Classification

AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

1 code implementation ICCV 2021 Rameswar Panda, Chun-Fu Chen, Quanfu Fan, Ximeng Sun, Kate Saenko, Aude Oliva, Rogerio Feris

Specifically, given a video segment, a multi-modal policy network is used to decide what modalities should be used for processing by the recognition model, with the goal of improving both accuracy and efficiency.

Video Recognition

RegionViT: Regional-to-Local Attention for Vision Transformers

3 code implementations ICLR 2022 Chun-Fu Chen, Rameswar Panda, Quanfu Fan

The regional-to-local attention includes two steps: first, the regional self-attention extract global information among all regional tokens and then the local self-attention exchanges the information among one regional token and the associated local tokens via self-attention.

Action Recognition Image Classification +2

Can An Image Classifier Suffice For Action Recognition?

1 code implementation ICLR 2022 Quanfu Fan, Chun-Fu, Chen, Rameswar Panda

We explore a new perspective on video understanding by casting the video recognition problem as an image recognition task.

Action Recognition Image Classification +2

Generating Realistic Physical Adversarial Examplesby Patch Transformer Network

no code implementations29 Sep 2021 Quanfu Fan, Kaidi Xu, Chun-Fu Chen, Sijia Liu, Gaoyuan Zhang, David Daniel Cox, Xue Lin

Physical adversarial attacks apply carefully crafted adversarial perturbations onto real objects to maliciously alter the prediction of object classifiers or detectors.

Object

CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection

no code implementations15 Apr 2022 Quanfu Fan, Yilai Li, Yuguang Yao, John Cohn, Sijia Liu, Seychelle M. Vos, Michael A. Cianfrocco

Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream structural biology techniques because of its ability to determine high-resolution structures of dynamic bio-molecules.

reinforcement-learning Reinforcement Learning (RL)

Temporal Relevance Analysis for Video Action Models

no code implementations25 Apr 2022 Quanfu Fan, Donghyun Kim, Chun-Fu, Chen, Stan Sclaroff, Kate Saenko, Sarah Adel Bargal

In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature.

Action Recognition

Distributed Adversarial Training to Robustify Deep Neural Networks at Scale

2 code implementations13 Jun 2022 Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, Sijia Liu

Spurred by that, we propose distributed adversarial training (DAT), a large-batch adversarial training framework implemented over multiple machines.

Distributed Optimization

Augmentation Learning for Semi-Supervised Classification

no code implementations3 Aug 2022 Tim Frommknecht, Pedro Alves Zipf, Quanfu Fan, Nina Shvetsova, Hilde Kuehne

As the accuracy for ImageNet and similar datasets increased over time, the performance on tasks beyond the classification of natural images is yet to be explored.

Classification Data Augmentation +1

Grafting Vision Transformers

no code implementations28 Oct 2022 Jongwoo Park, Kumara Kahatapitiya, Donghyun Kim, Shivchander Sudalairaj, Quanfu Fan, Michael S. Ryoo

In this paper, we present a simple and efficient add-on component (termed GrafT) that considers global dependencies and multi-scale information throughout the network, in both high- and low-resolution features alike.

Image Classification Instance Segmentation +3

Improve Video Representation with Temporal Adversarial Augmentation

no code implementations28 Apr 2023 Jinhao Duan, Quanfu Fan, Hao Cheng, Xiaoshuang Shi, Kaidi Xu

In this paper, we introduce Temporal Adversarial Augmentation (TA), a novel video augmentation technique that utilizes temporal attention.

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