7,482 research outputs found
Generative Adversarial Networks for Video-to-Video Domain Adaptation
Endoscopic videos from multicentres often have different imaging conditions,
e.g., color and illumination, which make the models trained on one domain
usually fail to generalize well to another. Domain adaptation is one of the
potential solutions to address the problem. However, few of existing works
focused on the translation of video-based data. In this work, we propose a
novel generative adversarial network (GAN), namely VideoGAN, to transfer the
video-based data across different domains. As the frames of a video may have
similar content and imaging conditions, the proposed VideoGAN has an X-shape
generator to preserve the intra-video consistency during translation.
Furthermore, a loss function, namely color histogram loss, is proposed to tune
the color distribution of each translated frame. Two colonoscopic datasets from
different centres, i.e., CVC-Clinic and ETIS-Larib, are adopted to evaluate the
performance of domain adaptation of our VideoGAN. Experimental results
demonstrate that the adapted colonoscopic video generated by our VideoGAN can
significantly boost the segmentation accuracy, i.e., an improvement of 5%, of
colorectal polyps on multicentre datasets. As our VideoGAN is a general network
architecture, we also evaluate its performance with the CamVid driving video
dataset on the cloudy-to-sunny translation task. Comprehensive experiments show
that the domain gap could be substantially narrowed down by our VideoGAN.Comment: Accepted by AAAI 202
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
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