Sign language recognition using convolutional neural networks

There is an undeniable communication problem between the Deaf community and the hearing majority. Innovations in automatic sign language recognition try to tear down this communication barrier. Our contribution considers a recognition system using the Microsoft Kinect, convolutional neural networks (CNNs) and GPU acceleration. Instead of constructing complex handcrafted features, CNNs are able to auto- mate the process of feature construction. We are able to recognize 20 Italian gestures with high accuracy. The predictive model is able to gen- eralize on users and surroundings not occurring during training with a cross-validation accuracy of 91.7%. Our model achieves a mean Jaccard Index of 0.789 in the ChaLearn 2014 Looking at People gesture spotting competition

Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition

This paper describes a novel method called Deep Dynamic Neural Networks (DDNN) for multimodal gesture recognition. A semi-supervised hierarchical dynamic framework based on a Hidden Markov Model (HMM) is proposed for simultaneous gesture segmentation and recognition where skeleton joint information, depth and RGB images, are the multimodal input observations. Unlike most traditional approaches that rely on the construction of complex handcrafted features, our approach learns high-level spatiotemporal representations using deep neural networks suited to the input modality: a Gaussian-Bernouilli Deep Belief Network (DBN) to handle skeletal dynamics, and a 3D Convolutional Neural Network (3DCNN) to manage and fuse batches of depth and RGB images. This is achieved through the modeling and learning of the emission probabilities of the HMM required to infer the gesture sequence. This purely data driven approach achieves a Jaccard index score of 0.81 in the ChaLearn LAP gesture spotting challenge. The performance is on par with a variety of state-of-the-art hand-tuned feature-based approaches and other learning-based methods, therefore opening the door to the use of deep learning techniques in order to further explore multimodal time series data

Assumption without representation: the unacknowledged abstraction from communities and social goods

We have not clearly acknowledged the abstraction from unpriceable “social goods” (derived from communities) which, different from private and public goods, simply disappear if it is attempted to market them. Separability from markets and economics has not been argued, much less established. Acknowledging communities would reinforce rather than undermine them, and thus facilitate the production of social goods. But it would also help economics by facilitating our understanding of – and response to – financial crises as well as environmental destruction and many social problems, and by reducing the alienation from economics often felt by students and the public

Sign classification in sign language Corpora with deep neural networks

Automatic and unconstrained sign language recognition (SLR) in image sequences remains a challenging problem. The variety of signers, backgrounds, sign executions and signer positions makes the development of SLR systems very challenging. Current methods try to alleviate this complexity by extracting engineered features to detect hand shapes, hand trajectories and facial expressions as an intermediate step for SLR. Our goal is to approach SLR based on feature learning rather than feature engineering. We tackle SLR using the recent advances in the domain of deep learning with deep neural networks. The problem is approached by classifying isolated signs from the Corpus VGT (Flemish Sign Language Corpus) and the Corpus NGT (Dutch Sign Language Corpus). Furthermore, we investigate cross-domain feature learning to boost the performance to cope with the fewer Corpus VGT annotations

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

Muddling through and policy analysis

In a variety of books and articles, both published and in process, I've been out pushing the idea of the “economics of muddling through “ as the description of the approach to policy that will become standard in economics over the next 20 or 30 years. The argument is both prescriptive - I argue muddling through is what should be done - and descriptive - I argue that muddling through is what is currently being done, although, like Monsieur Jourdain speaking prose in Moliere 's Le Bourgeois Gentilhomme, many economists don't recognize that that's what they are doing.1 If we've been muddlers for so long, why should we be willing to admit it now? I think there are three reasons. • First, there is a change occurring in formal theorizing in which the holy trinity -rationality, greed, and equilibrium - is being abandoned as required aspects of any model, and being replaced by a slightly broader trinity-purposeful behavior, enlightened self-interest, and sustainability.2 • Second, the work in the formal general equilibrium model built upon the foundation of the holy trinity has been thoroughly explored; all the low hanging fruit has been picked, and young theoretical researchers are naturally gravitating to less explored areas. • Third, today's muddling through is not your father's muddling through; it involves the use of a whole range of applied mathematics that is difficult to use unless we admit we are muddling. Today's muddling is technically impressive muddling and is afar cry from the armchair heuristics that characterized early muddling. The paper is organized as follows: First I consider the history of welfare economics, providing a narrative of how we got to where we are. Second, I briefly outline some important changes that are currently occurring in economics. Third, I expand on my reasons for believing that we are now ready to accept a “muddling through” characterization of applied policy, something we have not previously been willing to embrace.