Search Results for author: Chun-Fu Chen

Found 15 papers, 8 papers with code

Dynamic Network Quantization for Efficient Video Inference

no code implementations ICCV 2021 Ximeng Sun, Rameswar Panda, Chun-Fu Chen, Aude Oliva, Rogerio Feris, Kate Saenko

Deep convolutional networks have recently achieved great success in video recognition, yet their practical realization remains a challenge due to the large amount of computational resources required to achieve robust recognition.

Quantization Video Recognition

Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data

1 code implementation14 Jun 2021 Ashraful Islam, Chun-Fu Chen, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, Richard J. Radke

As the base dataset and unlabeled dataset are from different domains, projecting the target images in the class-domain of the base dataset with a fixed pretrained model might be sub-optimal.

cross-domain few-shot learning

RegionViT: Regional-to-Local Attention for Vision Transformers

1 code implementation4 Jun 2021 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 +1

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

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

6 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.

Classification General Classification +1

Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths

no code implementations2 Mar 2021 Ximeng Sun, Rameswar Panda, Chun-Fu Chen, Naigang Wang, Bowen Pan, Kailash Gopalakrishnan, Aude Oliva, Rogerio Feris, Kate Saenko

Second, to effectively transfer knowledge, we develop a dynamic block swapping method by randomly replacing the blocks in the lower-precision student network with the corresponding blocks in the higher-precision teacher network.

Classification Image Classification +3

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

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

no code implementations23 Jun 2020 Rameswar Panda, Michele Merler, Mayoore Jaiswal, Hui Wu, Kandan Ramakrishnan, Ulrich Finkler, Chun-Fu Chen, Minsik Cho, David Kung, Rogerio Feris, Bishwaranjan Bhattacharjee

The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset.

Neural Architecture Search

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 Classification +1

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 Recognition Speech Recognition

NISP: Pruning Networks using Neuron Importance Score Propagation

no code implementations CVPR 2018 Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I. Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, Larry S. Davis

In contrast, we argue that it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the "final response layer" (FRL), which is the second-to-last layer before classification, for a pruned network to retrain its predictive power.

Network Pruning

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