Search Results for author: Jianxin Ma

Found 21 papers, 8 papers with code

Controllable 3D Face Generation with Conditional Style Code Diffusion

1 code implementation21 Dec 2023 Xiaolong Shen, Jianxin Ma, Chang Zhou, Zongxin Yang

For 3D GAN inversion, we introduce two methods which aim to enhance the representation of style codes and alleviate 3D inconsistencies.

Data Augmentation Face Generation

JOTR: 3D Joint Contrastive Learning with Transformers for Occluded Human Mesh Recovery

1 code implementation ICCV 2023 Jiahao Li, Zongxin Yang, Xiaohan Wang, Jianxin Ma, Chang Zhou, Yi Yang

Our method includes an encoder-decoder transformer architecture to fuse 2D and 3D representations for achieving 2D$\&$3D aligned results in a coarse-to-fine manner and a novel 3D joint contrastive learning approach for adding explicitly global supervision for the 3D feature space.

Contrastive Learning Human Mesh Recovery

TransHuman: A Transformer-based Human Representation for Generalizable Neural Human Rendering

no code implementations ICCV 2023 Xiao Pan, Zongxin Yang, Jianxin Ma, Chang Zhou, Yi Yang

However, such SPC-based representation i) optimizes under the volatile observation space which leads to the pose-misalignment between training and inference stages, and ii) lacks the global relationships among human parts that is critical for handling the incomplete painted SMPL.

Global-to-Local Modeling for Video-based 3D Human Pose and Shape Estimation

1 code implementation CVPR 2023 Xiaolong Shen, Zongxin Yang, Xiaohan Wang, Jianxin Ma, Chang Zhou, Yi Yang

However, using a single kind of modeling structure is difficult to balance the learning of short-term and long-term temporal correlations, and may bias the network to one of them, leading to undesirable predictions like global location shift, temporal inconsistency, and insufficient local details.

3D human pose and shape estimation

OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models

1 code implementation8 Dec 2022 Jinze Bai, Rui Men, Hao Yang, Xuancheng Ren, Kai Dang, Yichang Zhang, Xiaohuan Zhou, Peng Wang, Sinan Tan, An Yang, Zeyu Cui, Yu Han, Shuai Bai, Wenbin Ge, Jianxin Ma, Junyang Lin, Jingren Zhou, Chang Zhou

As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data.

Multi-Task Learning

Pretrained Diffusion Models for Unified Human Motion Synthesis

no code implementations6 Dec 2022 Jianxin Ma, Shuai Bai, Chang Zhou

Generative modeling of human motion has broad applications in computer animation, virtual reality, and robotics.

Motion Synthesis Open-Ended Question Answering

M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems

no code implementations17 May 2022 Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, Hongxia Yang

Industrial recommender systems have been growing increasingly complex, may involve \emph{diverse domains} such as e-commerce products and user-generated contents, and can comprise \emph{a myriad of tasks} such as retrieval, ranking, explanation generation, and even AI-assisted content production.

Computational Efficiency Explanation Generation +3

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

1 code implementation11 Nov 2021 Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang

However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.

Cloud Computing Edge-computing

Learning to Rehearse in Long Sequence Memorization

no code implementations2 Jun 2021 Zhu Zhang, Chang Zhou, Jianxin Ma, Zhijie Lin, Jingren Zhou, Hongxia Yang, Zhou Zhao

Further, we design a history sampler to select informative fragments for rehearsal training, making the memory focus on the crucial information.

Memorization Question Answering +1

M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers

no code implementations NeurIPS 2021 Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang

Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.

Image Generation

UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis

no code implementations NeurIPS 2021 Zhu Zhang, Jianxin Ma, Chang Zhou, Rui Men, Zhikang Li, Ming Ding, Jie Tang, Jingren Zhou, Hongxia Yang

Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.

Image Generation

M6: A Chinese Multimodal Pretrainer

no code implementations1 Mar 2021 Junyang Lin, Rui Men, An Yang, Chang Zhou, Ming Ding, Yichang Zhang, Peng Wang, Ang Wang, Le Jiang, Xianyan Jia, Jie Zhang, Jianwei Zhang, Xu Zou, Zhikang Li, Xiaodong Deng, Jie Liu, Jinbao Xue, Huiling Zhou, Jianxin Ma, Jin Yu, Yong Li, Wei Lin, Jingren Zhou, Jie Tang, Hongxia Yang

In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.

Image Generation

Inductive Granger Causal Modeling for Multivariate Time Series

no code implementations10 Feb 2021 Yunfei Chu, Xiaowei Wang, Jianxin Ma, Kunyang Jia, Jingren Zhou, Hongxia Yang

To bridge this gap, we propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection on multivariate time series, which exploits the shared commonalities underlying the different individuals.

Time Series Time Series Analysis

Counterfactual Prediction for Bundle Treatment

no code implementations NeurIPS 2020 Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He

Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields.

counterfactual Decision Making +2

Disentangled Self-Supervision in Sequential Recommenders

1 code implementation23 Aug 2020 Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, Wenwu Zhu

There exist two challenges: i) reconstructing a future sequence containing many behaviors is exponentially harder than reconstructing a single next behavior, which can lead to difficulty in convergence, and ii) the sequence of all future behaviors can involve many intentions, not all of which may be predictable from the sequence of earlier behaviors.

Disentanglement

Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems

no code implementations20 May 2020 Chang Zhou, Jianxin Ma, Jianwei Zhang, Jingren Zhou, Hongxia Yang

Deep candidate generation (DCG) that narrows down the collection of relevant items from billions to hundreds via representation learning has become prevalent in industrial recommender systems.

Contrastive Learning Fairness +3

Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities

no code implementations ICLR 2020 Baichuan Yuan, Xiaowei Wang, Jianxin Ma, Chang Zhou, Andrea L. Bertozzi, Hongxia Yang

To bridge this gap, we introduce a declustering based hidden variable model that leads to an efficient inference procedure via a variational autoencoder (VAE).

Collaborative Filtering Point Processes +1

Learning Disentangled Representations for Recommendation

no code implementations NeurIPS 2019 Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu

Our approach achieves macro disentanglement by inferring the high-level concepts associated with user intentions (e. g., to buy a shirt or a cellphone), while capturing the preference of a user regarding the different concepts separately.

Decision Making Disentanglement +1

Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series

no code implementations25 Sep 2019 Yunfei Chu, Xiaowei Wang, Chunyan Feng, Jianxin Ma, Jingren Zhou, Hongxia Yang

Granger causal structure reconstruction is an emerging topic that can uncover causal relationship behind multivariate time series data.

Time Series Time Series Analysis

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