Search Results for author: Hanjiang Lai

Found 23 papers, 0 papers with code

ViT2Hash: Unsupervised Information-Preserving Hashing

no code implementations14 Jan 2022 Qinkang Gong, Liangdao Wang, Hanjiang Lai, Yan Pan, Jian Yin

Specifically, from pixels to continuous features, we first propose a feature-preserving module, using the corrupted image as input to reconstruct the original feature from the pre-trained ViT model and the complete image, so that the feature extractor can focus on preserving the meaningful information of original data.

Quantization

Learning Expensive Coordination: An Event-Based Deep RL Approach

no code implementations ICLR 2020 Zhenyu Shi*, Runsheng Yu*, Xinrun Wang*, Rundong Wang, Youzhi Zhang, Hanjiang Lai, Bo An

The main difficulties of expensive coordination are that i) the leader has to consider the long-term effect and predict the followers' behaviors when assigning bonuses and ii) the complex interactions between followers make the training process hard to converge, especially when the leader's policy changes with time.

Decision Making Multi-agent Reinforcement Learning

Controllable Face Aging

no code implementations20 Dec 2019 Haien Zeng, Hanjiang Lai, Jian Yin

Second, since the image may contain other unwanted attributes, an attribute disentanglement network is used to separate the individual embedding and learn the common embedding that contains information about the face attribute (e. g., race).

Disentanglement

Modal-aware Features for Multimodal Hashing

no code implementations19 Nov 2019 Haien Zeng, Hanjiang Lai, Hanlu Chu, Yong Tang, Jian Yin

The modal-aware operation consists of a kernel network and an attention network.

Simultaneous Region Localization and Hash Coding for Fine-grained Image Retrieval

no code implementations19 Nov 2019 Haien Zeng, Hanjiang Lai, Jian Yin

Fine-grained image hashing is a challenging problem due to the difficulties of discriminative region localization and hash code generation.

Code Generation Image Retrieval

Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning

no code implementations18 Nov 2019 Runsheng Yu, Zhenyu Shi, Xinrun Wang, Rundong Wang, Buhong Liu, Xinwen Hou, Hanjiang Lai, Bo An

Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized training and decentralized execution(CTDE) scheme, where all agents are trained together by the centralized valuenetwork and each agent execute its policy independently.

reinforcement-learning

Feature Pyramid Hashing

no code implementations4 Apr 2019 Yifan Yang, Libing Geng, Hanjiang Lai, Yan Pan, Jian Yin

In recent years, deep-networks-based hashing has become a leading approach for large-scale image retrieval.

Image Retrieval

Towards Multi-pose Guided Virtual Try-on Network

no code implementations ICCV 2019 Haoye Dong, Xiaodan Liang, Bochao Wang, Hanjiang Lai, Jia Zhu, Jian Yin

Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-on Network (MG-VTON) can generate a new person image after fitting the desired clothes into the input image and manipulating human poses.

Fashion Synthesis Human Parsing +2

Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis

no code implementations NeurIPS 2018 Haoye Dong, Xiaodan Liang, Ke Gong, Hanjiang Lai, Jia Zhu, Jian Yin

Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations.

Image Generation

Improving Deep Binary Embedding Networks by Order-aware Reweighting of Triplets

no code implementations17 Apr 2018 Jikai Chen, Hanjiang Lai, Libing Geng, Yan Pan

In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task.

Image Retrieval

Regularizing Deep Hashing Networks Using GAN Generated Fake Images

no code implementations26 Mar 2018 Libing Geng, Yan Pan, Jikai Chen, Hanjiang Lai

To address this issue, in this paper, we propose a simple two-stage pipeline to learn deep hashing models, by regularizing the deep hashing networks using fake images.

Image Retrieval

Personalized and Occupational-aware Age Progression by Generative Adversarial Networks

no code implementations26 Nov 2017 Siyu Zhou, Weiqiang Zhao, Jiashi Feng, Hanjiang Lai, Yan Pan, Jian Yin, Shuicheng Yan

Second, we propose a new occupational-aware adversarial face aging network, which learns human aging process under different occupations.

Human Aging

HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval

no code implementations26 Nov 2017 Xi Zhang, Siyu Zhou, Jiashi Feng, Hanjiang Lai, Bo Li, Yan Pan, Jian Yin, Shuicheng Yan

The proposed new adversarial network, HashGAN, consists of three building blocks: 1) the feature learning module to obtain feature representations, 2) the generative attention module to generate an attention mask, which is used to obtain the attended (foreground) and the unattended (background) feature representations, 3) the discriminative hash coding module to learn hash functions that preserve the similarities between different modalities.

Cross-Modal Retrieval

Transductive Zero-Shot Hashing via Coarse-to-Fine Similarity Mining

no code implementations8 Nov 2017 Hanjiang Lai, Yan Pan

It mainly consists of two building blocks in the proposed deep architecture: 1) a shared two-streams network, which the first stream operates on the source data and the second stream operates on the unlabeled data, to learn the effective common image representations, and 2) a coarse-to-fine module, which begins with finding the most representative images from target classes and then further detect similarities among these images, to transfer the similarities of the source data to the target data in a greedy fashion.

Transfer Learning

Improved Search in Hamming Space using Deep Multi-Index Hashing

no code implementations19 Oct 2017 Hanjiang Lai, Yan Pan

Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks.

Image Retrieval

Learning Adaptive Receptive Fields for Deep Image Parsing Network

no code implementations CVPR 2017 Zhen Wei, Yao Sun, Jinqiao Wang, Hanjiang Lai, Si Liu

In this paper, we introduce a novel approach to regulate receptive field in deep image parsing network automatically.

Face Parsing

Personalized Age Progression with Bi-level Aging Dictionary Learning

no code implementations4 Jun 2017 Xiangbo Shu, Jinhui Tang, Zechao Li, Hanjiang Lai, Liyan Zhang, Shuicheng Yan

Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e. g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process.

Dictionary Learning Face Verification

Instance-Aware Hashing for Multi-Label Image Retrieval

no code implementations10 Mar 2016 Hanjiang Lai, Pan Yan, Xiangbo Shu, Yunchao Wei, Shuicheng Yan

The instance-aware representations not only bring advantages to semantic hashing, but also can be used in category-aware hashing, in which an image is represented by multiple pieces of hash codes and each piece of code corresponds to a category.

Multi-Label Image Retrieval

Deep Recurrent Regression for Facial Landmark Detection

no code implementations30 Oct 2015 Hanjiang Lai, Shengtao Xiao, Yan Pan, Zhen Cui, Jiashi Feng, Chunyan Xu, Jian Yin, Shuicheng Yan

We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures.

Facial Landmark Detection

Personalized Age Progression with Aging Dictionary

no code implementations ICCV 2015 Xiangbo Shu, Jinhui Tang, Hanjiang Lai, Luoqi Liu, Shuicheng Yan

Second, it is challenging or even impossible to collect faces of all age groups for a particular subject, yet much easier and more practical to get face pairs from neighboring age groups.

Dictionary Learning Face Verification

Simultaneous Feature Learning and Hash Coding with Deep Neural Networks

no code implementations CVPR 2015 Hanjiang Lai, Yan Pan, Ye Liu, Shuicheng Yan

Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks.

Image Retrieval Quantization

A Divide-and-Conquer Method for Scalable Low-Rank Latent Matrix Pursuit

no code implementations CVPR 2013 Yan Pan, Hanjiang Lai, Cong Liu, Shuicheng Yan

To address this issue, we provide a scalable solution for large-scale low-rank latent matrix pursuit by a divide-andconquer method.

Event Detection Object Categorization

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