Search Results for author: Yue Bai

Found 22 papers, 13 papers with code

Rewrite the Stars

2 code implementations29 Mar 2024 Xu Ma, Xiyang Dai, Yue Bai, Yizhou Wang, Yun Fu

Recent studies have drawn attention to the untapped potential of the "star operation" (element-wise multiplication) in network design.

Don't Judge by the Look: Towards Motion Coherent Video Representation

1 code implementation14 Mar 2024 Yitian Zhang, Yue Bai, Huan Wang, Yizhou Wang, Yun Fu

Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is inefficient in practice.

Data Augmentation Object Recognition +2

Latent Graph Inference with Limited Supervision

no code implementations NeurIPS 2023 Jianglin Lu, Yi Xu, Huan Wang, Yue Bai, Yun Fu

We begin by defining the pivotal nodes as $k$-hop starved nodes, which can be identified based on a given adjacency matrix.

Frame Flexible Network

2 code implementations CVPR 2023 Yitian Zhang, Yue Bai, Chang Liu, Huan Wang, Sheng Li, Yun Fu

To fix this issue, we propose a general framework, named Frame Flexible Network (FFN), which not only enables the model to be evaluated at different frames to adjust its computation, but also reduces the memory costs of storing multiple models significantly.

Video Recognition

Making Reconstruction-based Method Great Again for Video Anomaly Detection

1 code implementation28 Jan 2023 Yizhou Wang, Can Qin, Yue Bai, Yi Xu, Xu Ma, Yun Fu

With the same perturbation magnitude, the testing reconstruction error of the normal frames lowers more than that of the abnormal frames, which contributes to mitigating the overfitting problem of reconstruction.

Anomaly Detection Optical Flow Estimation +1

Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning

2 code implementations12 Jan 2023 Huan Wang, Can Qin, Yue Bai, Yun Fu

The state of neural network pruning has been noticed to be unclear and even confusing for a while, largely due to "a lack of standardized benchmarks and metrics" [3].

Fairness Network Pruning

Look More but Care Less in Video Recognition

1 code implementation18 Nov 2022 Yitian Zhang, Yue Bai, Huan Wang, Yi Xu, Yun Fu

To tackle this problem, we propose Ample and Focal Network (AFNet), which is composed of two branches to utilize more frames but with less computation.

Action Recognition Video Recognition

Parameter-Efficient Masking Networks

1 code implementation13 Oct 2022 Yue Bai, Huan Wang, Xu Ma, Yitian Zhang, Zhiqiang Tao, Yun Fu

We validate the potential of PEMN learning masks on random weights with limited unique values and test its effectiveness for a new compression paradigm based on different network architectures.

Model Compression

Dual Lottery Ticket Hypothesis

1 code implementation ICLR 2022 Yue Bai, Huan Wang, Zhiqiang Tao, Kunpeng Li, Yun Fu

In this work, we regard the winning ticket from LTH as the subnetwork which is in trainable condition and its performance as our benchmark, then go from a complementary direction to articulate the Dual Lottery Ticket Hypothesis (DLTH): Randomly selected subnetworks from a randomly initialized dense network can be transformed into a trainable condition and achieve admirable performance compared with LTH -- random tickets in a given lottery pool can be transformed into winning tickets.

SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation

1 code implementation25 Nov 2021 Yizhou Wang, Can Qin, Rongzhe Wei, Yi Xu, Yue Bai, Yun Fu

Next we add adversarial perturbation to the transformed features to decrease their softmax scores of the predicted labels and design anomaly scores based on the predictive uncertainties of the classifier on these perturbed features.

Pseudo Label Self-Supervised Anomaly Detection +3

Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble

2 code implementations12 Oct 2021 Songyao Jiang, Bin Sun, Lichen Wang, Yue Bai, Kunpeng Li, Yun Fu

Current Sign Language Recognition (SLR) methods usually extract features via deep neural networks and suffer overfitting due to limited and noisy data.

Action Recognition Sign Language Recognition +1

Rethinking Again the Value of Network Pruning -- A Dynamical Isometry Perspective

no code implementations29 Sep 2021 Huan Wang, Can Qin, Yue Bai, Yun Fu

Several recent works questioned the value of inheriting weight in structured neural network pruning because they empirically found training from scratch can match or even outperform finetuning a pruned model.

Network Pruning

Dynamical Isometry: The Missing Ingredient for Neural Network Pruning

no code implementations12 May 2021 Huan Wang, Can Qin, Yue Bai, Yun Fu

This paper is meant to explain it through the lens of dynamical isometry [42].

Network Pruning

Skeleton Aware Multi-modal Sign Language Recognition

3 code implementations16 Mar 2021 Songyao Jiang, Bin Sun, Lichen Wang, Yue Bai, Kunpeng Li, Yun Fu

Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master.

Sign Language Recognition Skeleton Based Action Recognition

Recent Advances on Neural Network Pruning at Initialization

2 code implementations11 Mar 2021 Huan Wang, Can Qin, Yue Bai, Yulun Zhang, Yun Fu

Neural network pruning typically removes connections or neurons from a pretrained converged model; while a new pruning paradigm, pruning at initialization (PaI), attempts to prune a randomly initialized network.

Benchmarking Network Pruning

SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis

no code implementations7 Dec 2020 Yu Yin, Joseph P. Robinson, Songyao Jiang, Yue Bai, Can Qin, Yun Fu

Even as impressive milestones are achieved in synthesizing faces, the importance of preserving identity is needed in practice and should not be overlooked.

Face Generation Generative Adversarial Network +2

Collaborative Attention Mechanism for Multi-View Action Recognition

no code implementations14 Sep 2020 Yue Bai, Zhiqiang Tao, Lichen Wang, Sheng Li, Yu Yin, Yun Fu

Extensive experiments on four action datasets illustrate the proposed CAM achieves better results for each view and also boosts multi-view performance.

Action Recognition Representation Learning

Correlative Channel-Aware Fusion for Multi-View Time Series Classification

no code implementations24 Nov 2019 Yue Bai, Lichen Wang, Zhiqiang Tao, Sheng Li, Yun Fu

Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views.

Classification General Classification +3

Tuning-Free Disentanglement via Projection

no code implementations27 Jun 2019 Yue Bai, Leo L. Duan

In representation learning and non-linear dimension reduction, there is a huge interest to learn the 'disentangled' latent variables, where each sub-coordinate almost uniquely controls a facet of the observed data.

Dimensionality Reduction Disentanglement

Elastic Neural Networks for Classification

3 code implementations1 Oct 2018 Yi Zhou, Yue Bai, Shuvra S. Bhattacharyya, Heikki Huttunen

In this work we propose a framework for improving the performance of any deep neural network that may suffer from vanishing gradients.

Classification General Classification

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