Automatic parsing of sports videos with grammars

Motivated by the analogies between languages and sports videos, we introduce a novel approach for video parsing with grammars. It utilizes compiler techniques for integrating both semantic annotation and syntactic analysis to generate a semantic index of events and a table of content for a given sports video. The video sequence is first segmented and annotated by event detection with domain knowledge. A grammar-based parser is then used to identify the structure of the video content. Meanwhile, facilities for error handling are introduced which are particularly useful when the results of automatic parsing need to be adjusted. As a case study, we have developed a system for video parsing in the particular domain of TV diving programs. Experimental results indicate the proposed approach is effectiv

On-line processing of English which-questions by children and adults: a visual world paradigm study

Previous research has shown that children demonstrate similar sentence processing reflexes to those observed in adults, but they have difficulties revising an erroneous initial interpretation when they process garden-path sentences, passives, and wh -questions. We used the visual-world paradigm to examine children's use of syntactic and non-syntactic information to resolve syntactic ambiguity by extending our understanding of number features as a cue for interpretation to which -subject and which -object questions. We compared children's and adults’ eye-movements to understand how this information shapes children's commitment to and revision of possible interpretations of these questions. The results showed that English-speaking adults and children both exhibit an initial preference to interpret an object- which question as a subject question. While adults quickly override this preference, children take significantly longer, showing an overall processing difficulty for object questions. Crucially, their recovery from an initially erroneous interpretation is speeded when disambiguating number agreement features are present

ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation

We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally and vertically in both directions, encoding patches or activations, and providing relevant global information. Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features. Upsampling layers follow ReNet layers to recover the original image resolution in the final predictions. The proposed ReSeg architecture is efficient, flexible and suitable for a variety of semantic segmentation tasks. We evaluate ReSeg on several widely-used semantic segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving state-of-the-art performance. Results show that ReSeg can act as a suitable architecture for semantic segmentation tasks, and may have further applications in other structured prediction problems. The source code and model hyperparameters are available on https://github.com/fvisin/reseg.Comment: In CVPR Deep Vision Workshop, 201