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ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition
TLDR
Two large video multi-modal datasets for RGB and RGB-D gesture recognition are presented and the baseline method based on the bag of visual words model is presented, designed for gesture classification from segmented data.
ChaLearn Looking at People Challenge 2014: Dataset and Results
TLDR
In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition.
ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results
TLDR
A crowd-sourcing application was developed to collect and label data about the apparent age of people (as opposed to the real age) and in terms of cultural event recognition, one hundred categories had to be recognized.
ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results
TLDR
This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the first round of the competition, to automatically evaluate five “apparent” personality traits from videos of subjects speaking in front of a camera, by using human judgment.
On the Decoding Process in Ternary Error-Correcting Output Codes
TLDR
A taxonomy is presented that embeds all binary and ternary ECOC decoding strategies into four groups and shows that the zero symbol introduces two kinds of biases that require redefinition of the decoding design.
Multi-modal gesture recognition challenge 2013: dataset and results
TLDR
A challenge on multi-modal gesture recognition with 54 international teams, providing the audio, skeletal model, user mask, RGB and depth images, and outstanding results were obtained by the first ranked participants.
A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing
TLDR
A large-scale multi-modal dataset, namely CASIA-SURF, is introduced, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities and a new multi- modal fusion method is presented, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal.
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
TLDR
This paper proposes an extension of U-Net, Bi-directional ConvLSTM U- net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which the full advantages of U -Net, bi- directional Conv lSTM (BConvL STM) and the mechanism of dense convolutions are taken.
ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016
TLDR
A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age) and the citizen-science Zooniverse platform was used for the Faces of the World data.
Deep Structure Inference Network for Facial Action Unit Recognition
TLDR
A deep neural architecture is proposed that combines learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages to improve state-of-the-art performance.
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