19,538 research outputs found
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
The recent advances in deep learning have made it possible to generate
photo-realistic images by using neural networks and even to extrapolate video
frames from an input video clip. In this paper, for the sake of both furthering
this exploration and our own interest in a realistic application, we study
image-to-video translation and particularly focus on the videos of facial
expressions. This problem challenges the deep neural networks by another
temporal dimension comparing to the image-to-image translation. Moreover, its
single input image fails most existing video generation methods that rely on
recurrent models. We propose a user-controllable approach so as to generate
video clips of various lengths from a single face image. The lengths and types
of the expressions are controlled by users. To this end, we design a novel
neural network architecture that can incorporate the user input into its skip
connections and propose several improvements to the adversarial training method
for the neural network. Experiments and user studies verify the effectiveness
of our approach. Especially, we would like to highlight that even for the face
images in the wild (downloaded from the Web and the authors' own photos), our
model can generate high-quality facial expression videos of which about 50\%
are labeled as real by Amazon Mechanical Turk workers.Comment: 10 page
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