Region-based Activity Recognition Using Conditional GAN

Li, Xinyu; Zhang, Yanyi; Zhang, Jianyu; Chen, Yueyang; Li, Huangcan; Marsic, Ivan; Burd, Randall S.

doi:10.1145/3123266.3123365

Cited by 20 publications

(12 citation statements)

References 30 publications

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“…Maps from Satellite Images [36] super resolving face images [57] Unsupervise classification of street architectures [66] Feature Filter for EEG [74] Photos to Emojis CT Image Augmentation [37] HD movie from low-quality movie [58] Photograp h Editing Region-based Activity Recognition [38] Face Aging…”

Section: Architecture Basedmentioning

confidence: 99%

Generative Adversarial Networks : A Survey

kakkar¹,

Singh²

2021

Preprint

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Generative Modelling has been a very extensive area of research since it finds immense use cases across multiple domains. Various models have been proposed in the recent past including Fully Visible Belief Nets, NADE, MADE, Pixel RNN Variational Auto Encoders, Markov Chain, and Generative Adversarial Networks. Amongst all the models, Generative Adversarial Networks have been consistently showing huge potential and developments in the area of Art, Music, SemiSupervised learning, Handling Missing data, Drug Discovery, and unsupervised learning. This emerging technology has reshaped the research landscape in the field of generative modeling. The research in the area of Generative Adversarial Networks (GANs) was introduced by Ian J. Goodfellow et al in 2014 [1]. However, since its inception, various models have been proposed over the years and are considered state-of-the-art models in generative modeling. In this survey, we provide a comprehensive review of the original GAN model and its modified versions, covering broad topics on model architecture, Objective and Loss Functions, and applications. First, we summarize different architectures proposed along with Objective Functions and loss functions used. Second, we cover the evolution of GAN followed by summarizing comparative analysis for various GANs. Then, we review various applications proposed by various authors which are built over these architectures in different domains. Finally, the technical challenges and several promising directions are highlighted.

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Section: Architecture Basedmentioning

confidence: 99%

Generative Adversarial Networks : A Survey

kakkar¹,

Singh²

2021

Preprint

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“…While a binary map that exactly pinpoints the location of classdiscriminative features in the input is the most desirable and has been found to improve classification performance [28], such accurate maps require human-in-the-loop processing and cannot be readily available for most datasets. Our strategy also differs from feature muting [29] that sets a fixed number of features to zero, which can drastically change sample statistics and risk muting useful features through the fixed threshold.…”

Section: Masking Superficial Observationsmentioning

confidence: 99%

Robust Domain-Free Domain Generalization with Class-Aware Alignment

Zhang

Ragab

Sagarna

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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While deep neural networks demonstrate state-of-the-art performance on a variety of learning tasks, their performance relies on the assumption that train and test distributions are the same, which may not hold in real-world applications. Domain generalization addresses this issue by employing multiple source domains to build robust models that can generalize to unseen target domains subject to shifts in data distribution. In this paper, we propose Domain-Free Domain Generalization (DFDG), a model-agnostic method to achieve better generalization performance on the unseen test domain without the need for source domain labels. DFDG uses novel strategies to learn domain-invariant class-discriminative features. It aligns class relationships of samples through class-conditional soft labels, and uses saliency maps, traditionally developed for post-hoc analysis of image classification networks, to remove superficial observations from training inputs. DFDG obtains competitive performance on both time series sensor and image classification public datasets.

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“…Therefore, conditional GAN can be seen as an improvement in transforming unsupervised GAN into a supervised model. Later quantities of experiments showed that this method was very effective [16]- [18].…”

Section: B Conditional Generative Adversarial Networkmentioning

confidence: 99%

Image Smear Removal via Improved Conditional GAN and Semantic Network

Gao²,

Shen

et al. 2020

IEEE Access

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Image inpainting is one of the most important problems in the field of image algorithm, and it is also an effective preprocessing method for many other image applications. In this paper, we suggest an image decontamination method which is mainly used to remove mesh stains and also provide a data set for this task. To our knowledge, this work is the first attempt to solve this kind of problem. Specifically, the proposed method is composed of two phases: we first remove the mesh stains with an Improved Conditional Generative Adversarial Network, and then utilize a Semantic Network to fine tune the details. Experiments demonstrated that this two-stage model can remove the mesh stains. Results show that our method significantly outperforms existing methods and achieves superior inpainting results on challenging cases. INDEX TERMS Image restoration, deep learning, generative adversarial network.

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Region-based Activity Recognition Using Conditional GAN

Cited by 20 publications

References 30 publications

Generative Adversarial Networks : A Survey

Generative Adversarial Networks : A Survey

Robust Domain-Free Domain Generalization with Class-Aware Alignment

Image Smear Removal via Improved Conditional GAN and Semantic Network

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