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ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition

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.
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ChaLearn Looking at People Challenge 2014: Dataset and Results

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.
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ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results

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.
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ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results

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.
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Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

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.
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On the Decoding Process in Ternary Error-Correcting Output Codes

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.
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Multi-modal gesture recognition challenge 2013: dataset and results

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.
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A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing

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.
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Deep Structure Inference Network for Facial Action Unit Recognition

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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LSTA: Long Short-Term Attention for Egocentric Action Recognition

This paper proposes LSTA as a mechanism to focus on features from spatial relevant parts while attention is being tracked smoothly across the video sequence, achieving state-of-the-art performance on four standard benchmarks.
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