Search Results for author: Joe Yue-Hei Ng

Found 7 papers, 0 papers with code

EnsembleNet: End-to-End Optimization of Multi-headed Models

no code implementations24 May 2019 Hanhan Li, Joe Yue-Hei Ng, Paul Natsev

Ensembling is a universally useful approach to boost the performance of machine learning models.

TAN: Temporal Aggregation Network for Dense Multi-label Action Recognition

no code implementations14 Dec 2018 Xiyang Dai, Bharat Singh, Joe Yue-Hei Ng, Larry S. Davis

We present Temporal Aggregation Network (TAN) which decomposes 3D convolutions into spatial and temporal aggregation blocks.

Action Recognition

FASON: First and Second Order Information Fusion Network for Texture Recognition

no code implementations CVPR 2017 Xiyang Dai, Joe Yue-Hei Ng, Larry S. Davis

We then build a multi-level deep architecture to exploit the first and second order information within different convolutional layers.

ActionFlowNet: Learning Motion Representation for Action Recognition

no code implementations9 Dec 2016 Joe Yue-Hei Ng, Jonghyun Choi, Jan Neumann, Larry S. Davis

Even with the recent advances in convolutional neural networks (CNN) in various visual recognition tasks, the state-of-the-art action recognition system still relies on hand crafted motion feature such as optical flow to achieve the best performance.

Action Recognition Optical Flow Estimation

Generating Holistic 3D Scene Abstractions for Text-based Image Retrieval

no code implementations CVPR 2017 Ang Li, Jin Sun, Joe Yue-Hei Ng, Ruichi Yu, Vlad I. Morariu, Larry S. Davis

Since interactions between objects can be reduced to a limited set of atomic spatial relations in 3D, we study the possibility of inferring 3D structure from a text description rather than an image, applying physical relation models to synthesize holistic 3D abstract object layouts satisfying the spatial constraints present in a textual description.

Image Retrieval object-detection +1

Beyond Short Snippets: Deep Networks for Video Classification

no code implementations CVPR 2015 Joe Yue-Hei Ng, Matthew Hausknecht, Sudheendra Vijayanarasimhan, Oriol Vinyals, Rajat Monga, George Toderici

Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval.

Action Recognition Classification +2

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