Proceedings ArticleDOI
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
Luan Tran,Xi Yin,Xiaoming Liu +2 more
- pp 1283-1292
TLDR
Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.Abstract:
The large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator allows DR-GAN to learn a generative and discriminative representation, in addition to image synthesis. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. Third, DR-GAN can take one or multiple images as the input, and generate one unified representation along with an arbitrary number of synthetic images. Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.read more
Citations
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Journal ArticleDOI
Multi-View Face Recognition Via Well-Advised Pose Normalization Network
TL;DR: This work designs an end-to-end facial pose normalization network with adaptive weights on different objectives to exploit potentialities of various profile-front relationships and encourages intra-class compactness and inter-class separability between facial features by introducing quality-aware feature fusion.
Journal ArticleDOI
Transformation guided representation GAN for pose invariant face recognition
TL;DR: In this paper, the disentangled architecture GAN (D-GAN) performs frontal face synthesis via an encoder-decoder structure in the generator with the pose variations provided to the decoder and discriminator.
Journal ArticleDOI
Unsupervised Orthogonal Facial Representation Extraction via image reconstruction with correlation minimization
TL;DR: A deep Convolutional–Deconvolutional neural network is proposed for extracting identity representations and emotional representations from aligned faces and the need of identity labels is eliminated, and the applicable scope is extended.
Proceedings ArticleDOI
Unsupervised Single Image Dehazing via Disentangled Representation
TL;DR: Extensive experiment results on the public dehazing dataset RESIDE demonstrate that the proposed method outperforms state-of-the-art unsupervised methods, and can achieve comparable performance with the state of the art supervised methods.
Journal ArticleDOI
HiSA: Hierarchically Semantic Associating for Video Temporal Grounding
TL;DR: Hierarchically Semantic Associating is proposed, which aims to precisely align the video with language and obtain discriminative representation for further location regression, and significantly outperforms the state-of-the-art VTG methods.
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Conditional Generative Adversarial Nets
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