G. Hua

Publications248
h-index54
Citations11,587
Highly Influential Citations1,204
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Stacked Cross Attention for Image-Text Matching
TLDR
Stacked Cross Attention to discover the full latent alignments using both image regions and words in sentence as context and infer the image-text similarity achieves the state-of-the-art results on the MS-COCO and Flickr30K datasets. Expand
A convolutional neural network cascade for face detection
TLDR
This work proposes a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance, and introduces a CNN-based calibration stage after each of the detection stages in the cascade. Expand
Discriminative Learning of Local Image Descriptors
TLDR
A set of building blocks for constructing descriptors which can be combined together and jointly optimized so as to minimize the error of a nearest-neighbor classifier are described. Expand
Labeled Faces in the Wild: A Survey
TLDR
A review of the contributions to LFW for which the authors have provided results to the curators and the cross cutting topic of alignment and how it is used in various methods is reviewed. Expand
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
TLDR
This work proposes to jointly train a quantized, bit-operation-compatible DNN and its associated quantizers, as opposed to using fixed, handcrafted quantization schemes such as uniform or logarithmic quantization, to address the gap in prediction accuracy between the quantized model and the full-precision model. Expand
Neural Aggregation Network for Video Face Recognition
TLDR
This NAN is trained with a standard classification or verification loss without any extra supervision signal, and it is found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces. Expand
Ordinal Regression with Multiple Output CNN for Age Estimation
TLDR
This paper proposes an End-to-End learning approach to address ordinal regression problems using deep Convolutional Neural Network, which could simultaneously conduct feature learning and regression modeling, and achieves the state-of-the-art performance on both the MORPH and AFAD datasets. Expand
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing
TLDR
A deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering by estimating edges and reconstructing images using only cascaded convolutional layers arranged such that no handcrafted or application-specific image-processing components are required. Expand
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
We present variational generative adversarial networks, a general learning framework that combines a variational auto-encoder with a generative adversarial network, for synthesizing images in… Expand
Visual attribute transfer through deep image analogy
TLDR
The technique finds semantically-meaningful dense correspondences between two input images by adapting the notion of "image analogy" with features extracted from a Deep Convolutional Neutral Network for matching, and is called deep image analogy. Expand
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