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Found 4 papers, 3 papers with code
Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net
Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time.
Aligning where to see and what to tell: image caption with region-based attention and scene factorization
In this paper, we propose an image caption system that exploits the parallel structures between images and sentences.
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training
In contrast, our proposed architecture adopts deep convolutional neural networks with stacked temporal fusion layers as the feature extraction module, and bi-directional recurrent neural networks as the sequence learning module.
Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization
This work presents a weakly supervised framework with deep neural networks for vision-based continuous sign language recognition, where the ordered gloss labels but no exact temporal locations are available with the video of sign sentence, and the amount of labeled sentences for training is limited.
