Search Results for author: Xiao Dong

Found 17 papers, 2 papers with code

ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving

1 code implementation25 Apr 2024 Jiehui Huang, Xiao Dong, Wenhui Song, Hanhui Li, Jun Zhou, Yuhao Cheng, Shutao Liao, Long Chen, Yiqiang Yan, Shengcai Liao, Xiaodan Liang

ConsistentID comprises two key components: a multimodal facial prompt generator that combines facial features, corresponding facial descriptions and the overall facial context to enhance precision in facial details, and an ID-preservation network optimized through the facial attention localization strategy, aimed at preserving ID consistency in facial regions.

DRSM: efficient neural 4d decomposition for dynamic reconstruction in stationary monocular cameras

no code implementations1 Feb 2024 Weixing Xie, Xiao Dong, Yong Yang, Qiqin Lin, Jingze Chen, Junfeng Yao, Xiaohu Guo

With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology.

Dynamic Reconstruction Neural Rendering

UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning

no code implementations1 Jun 2023 Xiao Dong, Runhui Huang, XiaoYong Wei, Zequn Jie, Jianxing Yu, Jian Yin, Xiaodan Liang

Recent advances in vision-language pre-training have enabled machines to perform better in multimodal object discrimination (e. g., image-text semantic alignment) and image synthesis (e. g., text-to-image generation).

Contrastive Learning Retrieval +1

Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval

no code implementations17 Jun 2022 Xiao Dong, Xunlin Zhan, Yunchao Wei, XiaoYong Wei, YaoWei Wang, Minlong Lu, Xiaochun Cao, Xiaodan Liang

Our goal in this research is to study a more realistic environment in which we can conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories.


Worst-Case Dynamic Power Distribution Network Noise Prediction Using Convolutional Neural Network

no code implementations27 Apr 2022 Xiao Dong, Yufei Chen, Xunzhao Yin, Cheng Zhuo

Worst-case dynamic PDN noise analysis is an essential step in PDN sign-off to ensure the performance and reliability of chips.

BIG-bench Machine Learning

elBERto: Self-supervised Commonsense Learning for Question Answering

no code implementations17 Mar 2022 Xunlin Zhan, Yuan Li, Xiao Dong, Xiaodan Liang, Zhiting Hu, Lawrence Carin

Commonsense question answering requires reasoning about everyday situations and causes and effects implicit in context.

Question Answering Representation Learning +1

M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining

no code implementations CVPR 2022 Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael C. Kampffmeyer, XiaoYong Wei, Minlong Lu, YaoWei Wang, Xiaodan Liang

Despite the potential of multi-modal pre-training to learn highly discriminative feature representations from complementary data modalities, current progress is being slowed by the lack of large-scale modality-diverse datasets.

Contrastive Learning

Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining

1 code implementation ICCV 2021 Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, Xiaodan Liang

In this paper, we investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval among fine-grained product categories.


Pinpointing the Memory Behaviors of DNN Training

no code implementations1 Apr 2021 Jiansong Li, Xiao Dong, Guangli Li, Peng Zhao, Xueying Wang, Xiaobing Chen, Xianzhi Yu, Yongxin Yang, Zihan Jiang, Wei Cao, Lei Liu, Xiaobing Feng

The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators.

A Unified Joint Maximum Mean Discrepancy for Domain Adaptation

no code implementations25 Jan 2021 Wei Wang, Baopu Li, Shuhui Yang, Jing Sun, Zhengming Ding, Junyang Chen, Xiao Dong, Zhihui Wang, Haojie Li

From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence (discriminability) that benefits to classification, and it is sensitive to the label distribution shift when the label kernel is the weighted class conditional one.

Domain Adaptation

Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection

no code implementations25 Apr 2019 Xiao Dong, Lei Zhu, Xuemeng Song, Jingjing Li, Zhiyong Cheng

We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework.

feature selection

Understanding over-parameterized deep networks by geometrization

no code implementations11 Feb 2019 Xiao Dong, Ling Zhou

This can be regarded as a strong support of our proposal that geometrization is not only the bible for physics, it is also the key idea to understand deep learning systems.

Geometrization of deep networks for the interpretability of deep learning systems

no code implementations6 Jan 2019 Xiao Dong, Ling Zhou

By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.

Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge

no code implementations16 Dec 2018 Guangli Li, Lei Liu, Xueying Wang, Xiao Dong, Peng Zhao, Xiaobing Feng

By analyzing the characteristics of layers in DNNs, an auto-tuning neural network quantization framework for collaborative inference is proposed.

Collaborative Inference Quantization

Demystifying AlphaGo Zero as AlphaGo GAN

no code implementations24 Nov 2017 Xiao Dong, Jiasong Wu, Ling Zhou

The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a worldwide discussion of the future of our human society with a mixed mood of hope, anxiousness, excitement and fear.

How deep learning works --The geometry of deep learning

no code implementations30 Oct 2017 Xiao Dong, Jiasong Wu, Ling Zhou

Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective.

Template Matching

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