Search Results for author: Liangzhe Yuan

Found 8 papers, 7 papers with code

DeepLab2: A TensorFlow Library for Deep Labeling

1 code implementation17 Jun 2021 Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, Daniel Cremers, Laura Leal-Taixe, Alan L. Yuille, Florian Schroff, Hartwig Adam, Liang-Chieh Chen

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision.

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

2 code implementations22 Apr 2021 Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, Boqing Gong

We train VATT end-to-end from scratch using multimodal contrastive losses and evaluate its performance by the downstream tasks of video action recognition, audio event classification, image classification, and text-to-video retrieval.

 Ranked #1 on Action Classification on Moments in Time (using extra training data)

Action Classification Action Recognition +7

MoViNets: Mobile Video Networks for Efficient Video Recognition

2 code implementations CVPR 2021 Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong

We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference.

Action Classification Action Recognition +2

Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization

1 code implementation CVPR 2021 Long Zhao, Yuxiao Wang, Jiaping Zhao, Liangzhe Yuan, Jennifer J. Sun, Florian Schroff, Hartwig Adam, Xi Peng, Dimitris Metaxas, Ting Liu

To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition.

Action Recognition Contrastive Learning +1

Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion

1 code implementation CVPR 2019 Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney, Kostas Daniilidis

In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream.

Optical Flow Estimation

EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras

2 code implementations19 Feb 2018 Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney, Kostas Daniilidis

Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes.

Optical Flow Estimation

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