Search Results for author: Pichao Wang

Found 30 papers, 5 papers with code

CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

no code implementations13 Sep 2021 Tongkun Xu, Weihua Chen, Pichao Wang, Fan Wang, Hao Li, Rong Jin

Along with the pseudo labels, a weight-sharing triple-branch transformer framework is proposed to apply self-attention and cross-attention for source/target feature learning and source-target domain alignment, respectively.

Unsupervised Domain Adaptation

Scaled ReLU Matters for Training Vision Transformers

no code implementations8 Sep 2021 Pichao Wang, Xue Wang, Hao Luo, Jingkai Zhou, Zhipeng Zhou, Fan Wang, Hao Li, Rong Jin

In this paper, we further investigate this problem and extend the above conclusion: only early convolutions do not help for stable training, but the scaled ReLU operation in the \textit{convolutional stem} (\textit{conv-stem}) matters.

KVT: k-NN Attention for Boosting Vision Transformers

no code implementations28 May 2021 Pichao Wang, Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Wen Xie, Hao Li, Rong Jin

A key component in vision transformers is the fully-connected self-attention which is more powerful than CNNs in modelling long range dependencies.

TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection

no code implementations15 Apr 2021 Zitong Yu, Xiaobai Li, Pichao Wang, Guoying Zhao

3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from emergent 3D mask attacks.

Face Presentation Attack Detection Face Recognition

Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation

no code implementations30 Mar 2021 Shuning Chang, Pichao Wang, Fan Wang, Hao Li, Jiashi Feng

Temporal action proposal generation (TAPG) is a fundamental and challenging task in video understanding, especially in temporal action detection.

Action Detection Temporal Action Proposal Generation +1

Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition

1 code implementation1 Feb 2021 Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet.

Neural Architecture Search

Trear: Transformer-based RGB-D Egocentric Action Recognition

no code implementations5 Jan 2021 Xiangyu Li, Yonghong Hou, Pichao Wang, Zhimin Gao, Mingliang Xu, Wanqing Li

In this paper, we propose a \textbf{Tr}ansformer-based RGB-D \textbf{e}gocentric \textbf{a}ction \textbf{r}ecognition framework, called Trear.

Action Recognition Optical Flow Estimation

Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition

1 code implementation ICCV 2021 Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

To address this issue, instead of using an accuracy predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the architectures.

Neural Architecture Search

Transformer Guided Geometry Model for Flow-Based Unsupervised Visual Odometry

no code implementations8 Dec 2020 Xiangyu Li, Yonghong Hou, Pichao Wang, Zhimin Gao, Mingliang Xu, Wanqing Li

In this paper, we propose a method consisting of two camera pose estimators that deal with the information from pairwise images and a short sequence of images respectively.

Visual Odometry

SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching

no code implementations29 Oct 2020 Haoyuan Zhang, Yonghong Hou, Pichao Wang, Zihui Guo, Wanqing Li

The recently developed DARTS (Differentiable Architecture Search) is adopted to search for an effective network architecture that is built upon the two types of cells.

Action Recognition Skeleton Based Action Recognition

Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks

no code implementations17 Mar 2018 Pichao Wang, Wanqing Li, Zhimin Gao, Chang Tang, Philip Ogunbona

This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI), for both isolated and continuous action recognition.

3D Action Recognition Gesture Recognition

Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition

no code implementations5 Dec 2017 Pichao Wang, Wanqing Li, Jun Wan, Philip Ogunbona, Xinwang Liu

Differently from the conventional ConvNet that learns the deep separable features for homogeneous modality-based classification with only one softmax loss function, the c-ConvNet enhances the discriminative power of the deeply learned features and weakens the undesired modality discrepancy by jointly optimizing a ranking loss and a softmax loss for both homogeneous and heterogeneous modalities.

Action Recognition

RGB-D-based Human Motion Recognition with Deep Learning: A Survey

no code implementations31 Oct 2017 Pichao Wang, Wanqing Li, Philip Ogunbona, Jun Wan, Sergio Escalera

Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data.

Skeleton-based Action Recognition Using LSTM and CNN

no code implementations6 Jul 2017 Chuankun Li, Pichao Wang, Shuang Wang, Yonghong Hou, Wanqing Li

Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation.

Action Analysis Action Recognition +1

Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks

no code implementations CVPR 2017 Pichao Wang, Wanqing Li, Zhimin Gao, Yuyao Zhang, Chang Tang, Philip Ogunbona

Based on the scene flow vectors, we propose a new representation, namely, Scene Flow to Action Map (SFAM), that describes several long term spatio-temporal dynamics for action recognition.

3D Action Recognition

Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks

no code implementations7 Jan 2017 Pichao Wang, Wanqing Li, Song Liu, Zhimin Gao, Chang Tang, Philip Ogunbona

This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI).

General Classification Gesture Recognition

Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks

no code implementations30 Dec 2016 Pichao Wang, Wanqing Li, Chuankun Li, Yonghong Hou

Convolutional Neural Networks (ConvNets) have recently shown promising performance in many computer vision tasks, especially image-based recognition.

Action Recognition Skeleton Based Action Recognition

Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks

no code implementations8 Nov 2016 Pichao Wang, Zhaoyang Li, Yonghong Hou, Wanqing Li

Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image-based recognition.

Action Recognition

Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks

no code implementations22 Aug 2016 Pichao Wang, Wanqing Li, Song Liu, Yuyao Zhang, Zhimin Gao, Philip Ogunbona

This paper addresses the problem of continuous gesture recognition from sequences of depth maps using convolutional neutral networks (ConvNets).

General Classification Gesture Recognition

Combining ConvNets with Hand-Crafted Features for Action Recognition Based on an HMM-SVM Classifier

no code implementations1 Feb 2016 Pichao Wang, Zhaoyang Li, Yonghong Hou, Wanqing Li

This paper proposes a new framework for RGB-D-based action recognition that takes advantages of hand-designed features from skeleton data and deeply learned features from depth maps, and exploits effectively both the local and global temporal information.

Action Recognition

RGB-D-based Action Recognition Datasets: A Survey

no code implementations21 Jan 2016 Jing Zhang, Wanqing Li, Philip O. Ogunbona, Pichao Wang, Chang Tang

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010.

Action Recognition

Online Action Recognition based on Incremental Learning of Weighted Covariance Descriptors

no code implementations10 Nov 2015 Chang Tang, Pichao Wang, Wanqing Li

This paper presents a fast yet effective method to recognize actions from stream of noisy skeleton data, and a novel weighted covariance descriptor is adopted to accumulate evidence.

Action Recognition Incremental Learning

Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences

no code implementations20 Jan 2015 Pichao Wang, Wanqing Li, Zhimin Gao, Jing Zhang, Chang Tang, Philip Ogunbona

The results show that our approach can achieve state-of-the-art results on the individual datasets and without dramatical performance degradation on the Combined Dataset.

Action Recognition

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