Search Results for author: Mengyao Zhai

Found 8 papers, 1 papers with code

Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation

no code implementations CVPR 2021 Mengyao Zhai, Lei Chen, Greg Mori

Deep neural networks are susceptible to catastrophic forgetting: when encountering a new task, they can only remember the new task and fail to preserve its ability to accomplish previously learned tasks.

Continual Learning

Adaptive Appearance Rendering

1 code implementation24 Apr 2021 Mengyao Zhai, Ruizhi Deng, Jiacheng Chen, Lei Chen, Zhiwei Deng, Greg Mori

Hence, we develop an approach based on intermediate representations of poses and appearance: our pose-guided appearance rendering network firstly encodes the targets' poses using an encoder-decoder neural network.

Video Generation

Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation

no code implementations ECCV 2020 Mengyao Zhai, Lei Chen, JiaWei He, Megha Nawhal, Frederick Tung, Greg Mori

In contrast, we propose a parameter efficient framework, Piggyback GAN, which learns the current task by building a set of convolutional and deconvolutional filters that are factorized into filters of the models trained on previous tasks.

Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering

no code implementations5 Dec 2017 Mengyao Zhai, Jiacheng Chen, Ruizhi Deng, Lei Chen, Ligeng Zhu, Greg Mori

An architecture combining a hierarchical temporal model for predicting human poses and encoder-decoder convolutional neural networks for rendering target appearances is proposed.

Deep Learning of Appearance Models for Online Object Tracking

no code implementations9 Jul 2016 Mengyao Zhai, Mehrsan Javan Roshtkhari, Greg Mori

This paper introduces a novel deep learning based approach for vision based single target tracking.

Object Tracking

Deep Structured Models For Group Activity Recognition

no code implementations12 Jun 2015 Zhiwei Deng, Mengyao Zhai, Lei Chen, Yuhao Liu, Srikanth Muralidharan, Mehrsan Javan Roshtkhari, Greg Mori

This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes.

Group Activity Recognition

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