Search Results for author: Wenjie Pei

Found 21 papers, 7 papers with code

CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis

no code implementations ECCV 2020 Jiadong Liang, Wenjie Pei, Feng Lu

Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly.

Image Generation Semantic correspondence +1

Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain

no code implementations10 Oct 2021 Zengwei Yao, Wenjie Pei, Fanglin Chen, Guangming Lu, David Zhang

Existing methods for speech separation either transform the speech signals into frequency domain to perform separation or seek to learn a separable embedding space by constructing a latent domain based on convolutional filters.

Speech Recognition Speech Separation

Generative Memory-Guided Semantic Reasoning Model for Image Inpainting

no code implementations1 Oct 2021 Xin Feng, Wenjie Pei, Fengjun Li, Fanglin Chen, David Zhang, Guangming Lu

Most existing methods for image inpainting focus on learning the intra-image priors from the known regions of the current input image to infer the content of the corrupted regions in the same image.

Image Inpainting

Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders

no code implementations ICCV 2021 Jing Li, Di Kang, Wenjie Pei, Xuefei Zhe, Ying Zhang, Zhenyu He, Linchao Bao

In order to overcome this problem, we propose a novel conditional variational autoencoder (VAE) that explicitly models one-to-many audio-to-motion mapping by splitting the cross-modal latent code into shared code and motion-specific code.

Saliency-Associated Object Tracking

1 code implementation ICCV 2021 Zikun Zhou, Wenjie Pei, Xin Li, Hongpeng Wang, Feng Zheng, Zhenyu He

A potential limitation of such trackers is that not all patches are equally informative for tracking.

Object Tracking

Crop-Transform-Paste: Self-Supervised Learning for Visual Tracking

no code implementations21 Jun 2021 Xin Li, Wenjie Pei, Zikun Zhou, Zhenyu He, Huchuan Lu, Ming-Hsuan Yang

While deep-learning based methods for visual tracking have achieved substantial progress, these schemes entail large-scale and high-quality annotated data for sufficient training.

Representation Learning Self-Supervised Learning +1

Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images

1 code implementation9 Oct 2020 Xin Feng, Wenjie Pei, Zihui Jia, Fanglin Chen, David Zhang, Guangming Lu

In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise.

Image Dehazing Image Generation +3

CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis

1 code implementation18 Dec 2019 Jiadong Liang, Wenjie Pei, Feng Lu

Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly.

 Ranked #1 on Text-to-Image Generation on COCO (Inception score metric)

Image Generation Semantic correspondence +1

Push for Quantization: Deep Fisher Hashing

no code implementations31 Aug 2019 Yunqiang Li, Wenjie Pei, Yufei zha, Jan van Gemert

In this paper we push for quantization: We optimize maximum class separability in the binary space.

Quantization Semantic Similarity +1

Reflective Decoding Network for Image Captioning

no code implementations ICCV 2019 Lei Ke, Wenjie Pei, Ruiyu Li, Xiaoyong Shen, Yu-Wing Tai

State-of-the-art image captioning methods mostly focus on improving visual features, less attention has been paid to utilizing the inherent properties of language to boost captioning performance.

Image Captioning

Memory-Attended Recurrent Network for Video Captioning

1 code implementation CVPR 2019 Wenjie Pei, Jiyuan Zhang, Xiangrong Wang, Lei Ke, Xiaoyong Shen, Yu-Wing Tai

Typical techniques for video captioning follow the encoder-decoder framework, which can only focus on one source video being processed.

Video Captioning

Unsupervised Learning of Sequence Representations by Autoencoders

no code implementations3 Apr 2018 Wenjie Pei, David M. J. Tax

Sequence data is challenging for machine learning approaches, because the lengths of the sequences may vary between samples.

Attended End-to-end Architecture for Age Estimation from Facial Expression Videos

no code implementations23 Nov 2017 Wenjie Pei, Hamdi Dibeklioğlu, Tadas Baltrušaitis, David M. J. Tax

In this paper, we present an end-to-end architecture for age estimation, called Spatially-Indexed Attention Model (SIAM), which is able to simultaneously learn both the appearance and dynamics of age from raw videos of facial expressions.

Age Estimation

Interacting Attention-gated Recurrent Networks for Recommendation

no code implementations5 Sep 2017 Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, David M. J. Tax

In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions.

Temporal Attention-Gated Model for Robust Sequence Classification

1 code implementation CVPR 2017 Wenjie Pei, Tadas Baltrušaitis, David M. J. Tax, Louis-Philippe Morency

An important advantage of our approach is interpretability since the temporal attention weights provide a meaningful value for the salience of each time step in the sequence.

Classification General Classification +1

Modeling Time Series Similarity with Siamese Recurrent Networks

no code implementations15 Mar 2016 Wenjie Pei, David M. J. Tax, Laurens van der Maaten

Traditional techniques for measuring similarities between time series are based on handcrafted similarity measures, whereas more recent learning-based approaches cannot exploit external supervision.

General Classification Metric Learning +3

Time Series Classification using the Hidden-Unit Logistic Model

no code implementations16 Jun 2015 Wenjie Pei, Hamdi Dibeklioğlu, David M. J. Tax, Laurens van der Maaten

We present a new model for time series classification, called the hidden-unit logistic model, that uses binary stochastic hidden units to model latent structure in the data.

Action Recognition Action Unit Detection +6

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