9,233 research outputs found

    Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications

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    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

    Deep Generative Multimedia Children's Literature

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    Artistic work leveraging Machine Learning techniques is an increasingly popular endeavour for those with a creative lean. However, most work is done in a single domain: text, images, music, etc. In this work, I design a system for a machine learning created multimedia experience, specifically in the genre of children's literature. We detail the process for exclusively using publicly available pretrained deep neural network based models, I present multiple examples of the work my system creates, and I explore the problems associated in this area of creative work.Comment: Under review at AAAI 2023 Workshop on Creative AI Across Modalitie

    Polymeric Frameworks as Organic Semiconductors with Controlled Electronic Properties

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    The rational assembly of monomers, in principle, enables the design of a specific periodicity of polymeric frameworks, leading to a tailored set of electronic structure properties in these solid-state materials. The further development of these emerging systems requires a combination of both experimental and theoretical studies. Here, we investigated the electronic structures of two-dimensional polymeric frameworks based on triazine and benzene rings, by means of electrochemical techniques. The experimental density of states was obtained from quasi-open-circuit voltage measurements through galvanostatic intermittent titration technique, which we show to be in excellent agreement with first principles calculations performed for two and three-dimensional structures of these polymeric frameworks. These findings suggest that the electronic properties do not only depend on the number of stacked layers but also on the ratio of the different aromatic rings
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