GTNet: Generative Transfer Network for Zero-Shot Object Detection Article Swipe

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Shizhen Zhao , Changxin Gao , Yuanjie Shao , Lerenhan Li , Changqian Yu , Zhong Ji , Nong Sang ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1609/aaai.v34i07.6996

We propose a Generative Transfer Network (GTNet) for zero-shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Detection Module can learn large-scale seen domain knowledge. The Knowledge Transfer Module leverages a feature synthesizer to generate unseen class features, which are applied to train a new classification layer for the Object Detection Module. In order to synthesize features for each unseen class with both the intra-class variance and the IoU variance, we design an IoU-Aware Generative Adversarial Network (IoUGAN) as the feature synthesizer, which can be easily integrated into GTNet. Specifically, IoUGAN consists of three unit models: Class Feature Generating Unit (CFU), Foreground Feature Generating Unit (FFU), and Background Feature Generating Unit (BFU). CFU generates unseen features with the intra-class variance conditioned on the class semantic embeddings. FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively. We evaluate our method on three public datasets and the results demonstrate that our method performs favorably against the state-of-the-art ZSD approaches.

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Computer science Feature (linguistics) Class (philosophy) Artificial intelligence Object (grammar) Pattern recognition (psychology) Object detection Variance (accounting) Transfer (computing) Generative grammar Transfer of learning Feature extraction Computer vision Philosophy Linguistics Accounting Parallel computing Business

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Type: article
Language: en
Landing Page: https://doi.org/10.1609/aaai.v34i07.6996
PDF: https://ojs.aaai.org/index.php/AAAI/article/download/6996/6850
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Cited By: 48
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DOI: https://doi.org/10.1609/aaai.v34i07.6996

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Title: GTNet: Generative Transfer Network for Zero-Shot Object Detection

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Publication year: 2020

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Authors: Shizhen Zhao, Changxin Gao, Yuanjie Shao, Lerenhan Li, Changqian Yu, Zhong Ji, Nong Sang

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Landing page: https://doi.org/10.1609/aaai.v34i07.6996

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Concepts: Computer science, Feature (linguistics), Class (philosophy), Artificial intelligence, Object (grammar), Pattern recognition (psychology), Object detection, Variance (accounting), Transfer (computing), Generative grammar, Transfer of learning, Feature extraction, Computer vision, Philosophy, Linguistics, Accounting, Parallel computing, Business

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Cited by: 48

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