Search Results for author: Chang Zhou

Found 41 papers, 18 papers with code

Prompt Tuning for Generative Multimodal Pretrained Models

1 code implementation4 Aug 2022 Hao Yang, Junyang Lin, An Yang, Peng Wang, Chang Zhou, Hongxia Yang

Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural language pretraining and even vision pretraining.

Image Captioning Visual Entailment +2

Single Stage Virtual Try-on via Deformable Attention Flows

1 code implementation19 Jul 2022 Shuai Bai, Huiling Zhou, Zhikang Li, Chang Zhou, Hongxia Yang

Virtual try-on aims to generate a photo-realistic fitting result given an in-shop garment and a reference person image.

Image Animation Virtual Try-on

Instance-wise Prompt Tuning for Pretrained Language Models

no code implementations4 Jun 2022 Yuezihan Jiang, Hao Yang, Junyang Lin, Hanyu Zhao, An Yang, Chang Zhou, Hongxia Yang, Zhi Yang, Bin Cui

Prompt Learning has recently gained great popularity in bridging the gap between pretraining tasks and various downstream tasks.

Pretrained Language Models

M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing

no code implementations24 May 2022 Zhikang Li, Huiling Zhou, Shuai Bai, Peike Li, Chang Zhou, Hongxia Yang

The fashion industry has diverse applications in multi-modal image generation and editing.

Image Generation

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.

Explanation Generation Language Modelling +2

In-N-Out Generative Learning for Dense Unsupervised Video Segmentation

no code implementations29 Mar 2022 Xiao Pan, Peike Li, Zongxin Yang, Huiling Zhou, Chang Zhou, Hongxia Yang, Jingren Zhou, Yi Yang

As to pixel-level optimization, we perform in-view masked image modeling on patch tokens, which recovers the corrupted parts of an image via inferring its fine-grained structure, and we term it as in-generative learning.

Contrastive Learning Semantic Segmentation +3

Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)

no code implementations23 Mar 2022 Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, Longbo Huang

Recently, it has been observed that the best uni-modal network outperforms the jointly trained multi-modal network, which is counter-intuitive since multiple signals generally bring more information.

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks

1 code implementation30 Dec 2021 Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang

Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements.

M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining

no code implementations8 Oct 2021 Junyang Lin, An Yang, Jinze Bai, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Yong Li, Wei Lin, Jingren Zhou, Hongxia Yang

Recent expeditious developments in deep learning algorithms, distributed training, and even hardware design for large models have enabled training extreme-scale models, say GPT-3 and Switch Transformer possessing hundreds of billions or even trillions of parameters.

Causal Attention for Unbiased Visual Recognition

1 code implementation ICCV 2021 Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang

Attention module does not always help deep models learn causal features that are robust in any confounding context, e. g., a foreground object feature is invariant to different backgrounds.

CSRNet: Cascaded Selective Resolution Network for Real-time Semantic Segmentation

no code implementations8 Jun 2021 Jingjing Xiong, Lai-Man Po, Wing-Yin Yu, Chang Zhou, Pengfei Xian, Weifeng Ou

Real-time semantic segmentation has received considerable attention due to growing demands in many practical applications, such as autonomous vehicles, robotics, etc.

Autonomous Vehicles Real-Time Semantic Segmentation

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.

Question Answering Video Question Answering

M6-T: Exploring Sparse Expert Models and Beyond

no code implementations31 May 2021 An Yang, Junyang Lin, Rui Men, Chang Zhou, Le Jiang, Xianyan Jia, Ang Wang, Jie Zhang, Jiamang Wang, Yong Li, Di Zhang, Wei Lin, Lin Qu, Jingren Zhou, Hongxia Yang

Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scaling.

2048

Connecting Language and Vision for Natural Language-Based Vehicle Retrieval

1 code implementation31 May 2021 Shuai Bai, Zhedong Zheng, Xiaohan Wang, Junyang Lin, Zhu Zhang, Chang Zhou, Yi Yang, Hongxia Yang

In this paper, we apply one new modality, i. e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario.

Language Modelling Management +1

Controllable Gradient Item Retrieval

1 code implementation31 May 2021 Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang, Jingrui He

A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence.

Disentanglement

Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation

no code implementations Findings (ACL) 2021 Peng Wang, Junyang Lin, An Yang, Chang Zhou, Yichang Zhang, Jingren Zhou, Hongxia Yang

Experimental results demonstrate that our method outperforms the previous state-of-the-art methods in both automatic and human evaluation, especially on coverage and faithfulness.

Table-to-Text Generation

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

CogView: Mastering Text-to-Image Generation via Transformers

3 code implementations NeurIPS 2021 Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang

Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding.

Ranked #22 on Text-to-Image Generation on COCO (using extra training data)

Super-Resolution Text to image generation +1

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

Local Clustering Graph Neural Networks

no code implementations1 Jan 2021 Jiezhong Qiu, Yukuo Cen, Qibin Chen, Chang Zhou, Jingren Zhou, Hongxia Yang, Jie Tang

Based on the theoretical analysis, we propose Local Clustering Graph Neural Networks (LCGNN), a GNN learning paradigm that utilizes local clustering to efficiently search for small but compact subgraphs for GNN training and inference.

Continual Memory: Can We Reason After Long-Term Memorization?

no code implementations1 Jan 2021 Zhu Zhang, Chang Zhou, Zhou Zhao, Zhijie Lin, Jingren Zhou, Hongxia Yang

Existing reasoning tasks often follow the setting of "reasoning while experiencing", which has an important assumption that the raw contents can be always accessed while reasoning.

VideoFlow: A Framework for Building Visual Analysis Pipelines

no code implementations1 Jan 2021 Yue Wu, Jianqiang Huang, Jiangjie Zhen, Guokun Wang, Chen Shen, Chang Zhou, Xian-Sheng Hua

The past years have witnessed an explosion of deep learning frameworks like PyTorch and TensorFlow since the success of deep neural networks.

CogLTX: Applying BERT to Long Texts

1 code implementation NeurIPS 2020 Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang

BERTs are incapable of processing long texts due to its quadratically increasing memory and time consumption.

Text Classification

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

Understanding Negative Sampling in Graph Representation Learning

2 code implementations20 May 2020 Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, Jie Tang

To the best of our knowledge, we are the first to derive the theory and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution.

Graph Learning Graph Representation Learning +2

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

ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning

no code implementations2 Dec 2019 Chunnan Wang, Hongzhi Wang, Chang Zhou, Hanxiao Chen

Motivated by this, we propose ExperienceThinking algorithm to quickly find the best possible hyperparameter configuration of machine learning algorithms within a few configuration evaluations.

BIG-bench Machine Learning Hyperparameter Optimization +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

Dimensional Reweighting Graph Convolution Networks

no code implementations25 Sep 2019 Xu Zou, Qiuye Jia, Jianwei Zhang, Chang Zhou, Zijun Yao, Hongxia Yang, Jie Tang

In this paper, we propose a method named Dimensional reweighting Graph Convolutional Networks (DrGCNs), to tackle the problem of variance between dimensional information in the node representations of GCNs.

Node Classification

Dimensional Reweighting Graph Convolutional Networks

2 code implementations4 Jul 2019 Xu Zou, Qiuye Jia, Jianwei Zhang, Chang Zhou, Hongxia Yang, Jie Tang

Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs.

Node Classification

Cognitive Knowledge Graph Reasoning for One-shot Relational Learning

1 code implementation13 Jun 2019 Zhengxiao Du, Chang Zhou, Ming Ding, Hongxia Yang, Jie Tang

Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently.

Knowledge Graphs Relational Reasoning

Personalized Bundle List Recommendation

no code implementations3 Apr 2019 Jinze Bai, Chang Zhou, Junshuai Song, Xiaoru Qu, Weiting An, Zhao Li, Jun Gao

In particular, BGN improves the precision of the best competitors by 16\% on average while maintaining the highest diversity on four datasets, and yields a 3. 85x improvement of response time over the best competitors in the bundle list recommendation problem.

Marketing Point Processes +1

AliGraph: A Comprehensive Graph Neural Network Platform

no code implementations23 Feb 2019 Rong Zhu, Kun Zhao, Hongxia Yang, Wei. Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou

An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements.

Distributed, Parallel, and Cluster Computing

Deep Interest Evolution Network for Click-Through Rate Prediction

13 code implementations11 Sep 2018 Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, Kun Gai

Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt

Click-Through Rate Prediction

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