1,747 research outputs found
Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting and
useful, but current AI systems are still far from this goal. However, in recent
years generic and powerful recurrent neural network architectures have been
developed to learn discriminative text feature representations. Meanwhile, deep
convolutional generative adversarial networks (GANs) have begun to generate
highly compelling images of specific categories, such as faces, album covers,
and room interiors. In this work, we develop a novel deep architecture and GAN
formulation to effectively bridge these advances in text and image model- ing,
translating visual concepts from characters to pixels. We demonstrate the
capability of our model to generate plausible images of birds and flowers from
detailed text descriptions.Comment: ICML 201
Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL) proposes one solution to this problem. ZSL trains a model once, and generalizes to new tasks whose classes are not present in the training dataset. We propose the first end-to-end algorithm for ZSL in video classification. Our training procedure builds on insights from recent video classification literature and uses a trainable 3D CNN to learn the visual features. This is in contrast to previous video ZSL methods, which use pretrained feature extractors. We also extend the current benchmarking paradigm: Previous techniques aim to make the test task unknown at training time but fall short of this goal. We encourage domain shift across training and test data and disallow tailoring a ZSL model to a specific test dataset. We outperform the state-of-the-art by a wide margin. Our code, evaluation procedure and model weights are available at this http URL
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Understanding human activity and being able to explain it in detail surpasses
mere action classification by far in both complexity and value. The challenge
is thus to describe an activity on the basis of its most fundamental
constituents, the individual postures and their distinctive transitions.
Supervised learning of such a fine-grained representation based on elementary
poses is very tedious and does not scale. Therefore, we propose a completely
unsupervised deep learning procedure based solely on video sequences, which
starts from scratch without requiring pre-trained networks, predefined body
models, or keypoints. A combinatorial sequence matching algorithm proposes
relations between frames from subsets of the training data, while a CNN is
reconciling the transitivity conflicts of the different subsets to learn a
single concerted pose embedding despite changes in appearance across sequences.
Without any manual annotation, the model learns a structured representation of
postures and their temporal development. The model not only enables retrieval
of similar postures but also temporal super-resolution. Additionally, based on
a recurrent formulation, next frames can be synthesized.Comment: Accepted by ICCV 201
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