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Understanding Beauty via Deep Facial Features
The concept of beauty has been debated by philosophers and psychologists for centuries, but most definitions are subjective and metaphysical, and deficit in accuracy, generality, and scalability.
Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention
It has been applied to numerous domains, such as data augmentation, domain adaptation, and unsupervised training.
Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation
Recognizing fingerspelling is challenging for a number of reasons: It involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected.
American Sign Language fingerspelling recognition from video: Methods for unrestricted recognition and signer-independence
In this thesis, we study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL).
Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation
Previous work has shown that it is possible to achieve almost 90% accuracies on fingerspelling recognition in a signer-dependent setting.
Sparse Coding for Learning Interpretable Spatio-Temporal Primitives
Sparse coding has recently become a popular approach in computer vision to learn dictionaries of natural images.
