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Hemerson Tacon

Researcher at Universidade Federal de Juiz de Fora

Publications -  7
Citations -  37

Hemerson Tacon is an academic researcher from Universidade Federal de Juiz de Fora. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 3, co-authored 7 publications receiving 24 citations.

Papers
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Proceedings ArticleDOI

Multi-stream Convolutional Neural Networks for Action Recognition in Video Sequences Based on Adaptive Visual Rhythms

TL;DR: A multi-stream network is the architecture of choice to incorporate temporal information, since it may benefit from pre-trained deep networks for images and from handcrafted features for initialization, and its training cost is usually lower than video-based networks.
Book ChapterDOI

Human action recognition using convolutional neural networks with symmetric time extension of visual rhythms

TL;DR: This work proposes the usage of multiple Visual Rhythm crops, symmetrically extended in time and separated by a fixed stride, which provide a 2D representation of the video volume matching the fixed input size of the 2D Convolutional Neural Network employed.
Journal ArticleDOI

Weighted voting of multi-stream convolutional neural networks for video-based action recognition using optical flow rhythms

TL;DR: A multi-stream architecture based on the weighted voting of convolutional neural networks to deal with the problem of recognizing human actions in videos is proposed, with a new stream, Optical Flow Rhythm, besides using other streams for diversity.
Book ChapterDOI

Action Recognition in Videos Using Multi-stream Convolutional Neural Networks

TL;DR: A different pre-training procedure for the latter stream is developed using visual rhythm images extracted from a large and challenging video dataset, the Kinetics, which aims to classify trimmed videos based on the action being performed by one or more agents.
Proceedings ArticleDOI

Learnable Visual Rhythms Based on the Stacking of Convolutional Neural Networks for Action Recognition

TL;DR: This work addresses the problem of human action recognition in videos through a multi-stream network that incorporates both spatial and temporal information, and employs a deep network to extract features from the video frames in order to generate the rhythm.