Aäron van den Oord

Beyond temporal pooling : recurrence and temporal convolutions for gesture recognition in video

Recent studies have demonstrated the power of recurrent neural networks for machine translation, image captioning and speech recognition. For the task of capturing temporal structure in video, however, there still remain numerous open research questions. Current research suggests using a simple temporal feature pooling strategy to take into account the temporal aspect of video. We demonstrate that this method is not sufficient for gesture recognition, where temporal information is more discriminative compared to general video classification tasks. We explore deep architectures for gesture recognition in video and propose a new end-to-end trainable neural network architecture incorporating temporal convolutions and bidirectional recurrence. Our main contributions are twofold; first, we show that recurrence is crucial for this task; second, we show that adding temporal convolutions leads to significant improvements. We evaluate the different approaches on the Montalbano gesture recognition dataset, where we achieve state-of-the-art results.

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Learning a piecewise linear transform coding scheme for images

Gaussian mixture models are among the most widely accepted methods for clustering and probability density estimation. Recently it has been shown that these statistical methods are perfectly suited for learning patch-based image priors for various image restoration problems. In this paper we investigate the use of GMMs for image compression. A piecewise linear transform coding scheme based on Vector Quantization is proposed. In this scheme two different learning algorithms for GMMs are considered and compared. Experimental results demonstrate that the proposed techniques outperform JPEG, with results comparable to JPEG2000 for a broad class of images.