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08/31/2023
TouchStone: Evaluating Vision-Language Models by Language Models
Large vision-language models (LVLMs) have recently witnessed rapid advan...
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08/24/2023
Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities
We introduce the Qwen-VL series, a set of large-scale vision-language mo...
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08/14/2023
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Foundation language models obtain the instruction-following ability thro...
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08/03/2023
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
Mathematical reasoning is a challenging task for large language models (...
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07/31/2023
JOTR: 3D Joint Contrastive Learning with Transformers for Occluded Human Mesh Recovery
In this study, we focus on the problem of 3D human mesh recovery from a ...
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07/23/2023
TransHuman: A Transformer-based Human Representation for Generalizable Neural Human Rendering
In this paper, we focus on the task of generalizable neural human render...
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05/24/2023
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
The answering quality of an aligned large language model (LLM) can be dr...
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05/18/2023
ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
In this work, we explore a scalable way for building a general represent...
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04/26/2023
Video Frame Interpolation with Densely Queried Bilateral Correlation
Video Frame Interpolation (VFI) aims to synthesize non-existent intermed...
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03/26/2023
Global-to-Local Modeling for Video-based 3D Human Pose and Shape Estimation
Video-based 3D human pose and shape estimations are evaluated by intra-f...
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02/17/2023
Binary Embedding-based Retrieval at Tencent
Large-scale embedding-based retrieval (EBR) is the cornerstone of search...
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12/19/2022
Transferring General Multimodal Pretrained Models to Text Recognition
This paper proposes a new method, OFA-OCR, to transfer multimodal pretra...
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12/08/2022
OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models
Generalist models, which are capable of performing diverse multi-modal t...
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12/06/2022
Pretrained Diffusion Models for Unified Human Motion Synthesis
Generative modeling of human motion has broad applications in computer a...
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11/29/2022
MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition
In this paper, we propose a novel multi-modal multi-task encoder-decoder...
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11/26/2022
Contextual Expressive Text-to-Speech
The goal of expressive Text-to-speech (TTS) is to synthesize natural spe...
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11/02/2022
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
The tremendous success of CLIP (Radford et al., 2021) has promoted the r...
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10/23/2022
Respecting Transfer Gap in Knowledge Distillation
Knowledge distillation (KD) is essentially a process of transferring a t...
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08/04/2022
Prompt Tuning for Generative Multimodal Pretrained Models
Prompt tuning has become a new paradigm for model tuning and it has demo...
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07/19/2022
Single Stage Virtual Try-on via Deformable Attention Flows
Virtual try-on aims to generate a photo-realistic fitting result given a...
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06/04/2022
Instance-wise Prompt Tuning for Pretrained Language Models
Prompt Learning has recently gained great popularity in bridging the gap...
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05/24/2022
M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing
The fashion industry has diverse applications in multi-modal image gener...
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05/17/2022
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
Industrial recommender systems have been growing increasingly complex, m...
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03/29/2022
In-N-Out Generative Learning for Dense Unsupervised Video Segmentation
In this paper, we focus on the unsupervised Video Object Segmentation (V...
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03/23/2022
Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)
Despite the remarkable success of deep multi-modal learning in practice,...
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02/07/2022
Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
In this work, we pursue a unified paradigm for multimodal pretraining to...
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12/30/2021
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Heterogeneous graph neural networks (HGNNs) have been blossoming in rece...
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10/08/2021
M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining
Recent expeditious developments in deep learning algorithms, distributed...
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08/19/2021
Causal Attention for Unbiased Visual Recognition
Attention module does not always help deep models learn causal features ...
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06/08/2021
CSRNet: Cascaded Selective Resolution Network for Real-time Semantic Segmentation
Real-time semantic segmentation has received considerable attention due ...
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06/02/2021
Learning to Rehearse in Long Sequence Memorization
Existing reasoning tasks often have an important assumption that the inp...
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05/31/2021
Controllable Gradient Item Retrieval
In this paper, we identify and study an important problem of gradient it...
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05/31/2021
Exploring Sparse Expert Models and Beyond
Mixture-of-Experts (MoE) models can achieve promising results with outra...
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05/31/2021
Connecting Language and Vision for Natural Language-Based Vehicle Retrieval
Vehicle search is one basic task for the efficient traffic management in...
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05/31/2021
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation
Table-to-text generation refers to generating a descriptive text from a ...
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05/29/2021
UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis
Conditional image synthesis aims to create an image according to some mu...
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05/26/2021
CogView: Mastering Text-to-Image Generation via Transformers
Text-to-Image generation in the general domain has long been an open pro...
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03/01/2021
CogDL: An Extensive Toolkit for Deep Learning on Graphs
Graph representation learning aims to learn low-dimensional node embeddi...
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03/01/2021
M6: A Chinese Multimodal Pretrainer
In this work, we construct the largest dataset for multimodal pretrainin...
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05/20/2020
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
Deep candidate generation (DCG) that narrows down the collection of rele...
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05/20/2020
Contrastive Learning for Debiased Candidate Generation at Scale
Deep candidate generation has become an increasingly popular choice depl...
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05/20/2020
Understanding Negative Sampling in Graph Representation Learning
Graph representation learning has been extensively studied in recent yea...
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05/19/2020
Controllable Multi-Interest Framework for Recommendation
Recently, neural networks have been widely used in e-commerce recommende...
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12/02/2019
ExperienceThinking: Hyperparameter Optimization with Budget Constraints
The problem of hyperparameter optimization exists widely in the real lif...
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10/31/2019
Learning Disentangled Representations for Recommendation
User behavior data in recommender systems are driven by the complex inte...
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07/04/2019
Dimensional Reweighting Graph Convolutional Networks
Graph Convolution Networks (GCNs) are becoming more and more popular for...
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06/13/2019
Cognitive Knowledge Graph Reasoning for One-shot Relational Learning
Inferring new facts from existing knowledge graphs (KG) with explainable...
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05/14/2019
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
We propose a new CogQA framework for multi-hop question answering in web...
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04/03/2019
Personalized Bundle List Recommendation
Product bundling, offering a combination of items to customers, is one o...
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02/23/2019