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Found 41 papers, 18 papers with code
Prompt Tuning for Generative Multimodal Pretrained Models
Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural language pretraining and even vision pretraining.
Ranked #2 on
Visual Entailment
on SNLI-VE test
Single Stage Virtual Try-on via Deformable Attention Flows
Virtual try-on aims to generate a photo-realistic fitting result given an in-shop garment and a reference person image.
Instance-wise Prompt Tuning for Pretrained Language Models
Prompt Learning has recently gained great popularity in bridging the gap between pretraining tasks and various downstream tasks.
M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing
The fashion industry has diverse applications in multi-modal image generation and editing.
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
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.
In-N-Out Generative Learning for Dense Unsupervised Video Segmentation
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.
Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)
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.
OFA: 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 break the scaffolds of complex task/modality-specific customization.
Ranked #1 on
Visual Question Answering
on VQA v2 test-std
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
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
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
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
Real-time semantic segmentation has received considerable attention due to growing demands in many practical applications, such as autonomous vehicles, robotics, etc.
Learning to Rehearse in Long Sequence Memorization
Further, we design a history sampler to select informative fragments for rehearsal training, making the memory focus on the crucial information.
M6-T: Exploring Sparse Expert Models and Beyond
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.
Connecting Language and Vision for Natural Language-Based Vehicle Retrieval
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.
Controllable Gradient Item Retrieval
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.
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation
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.
M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers
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.
CogView: Mastering Text-to-Image Generation via Transformers
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)
UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis
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.
CogDL: A Toolkit for Deep Learning on Graphs
Deep learning on graphs has attracted tremendous attention from the graph learning community in recent years.
M6: A Chinese Multimodal Pretrainer
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.
Local Clustering Graph Neural Networks
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?
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
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
BERTs are incapable of processing long texts due to its quadratically increasing memory and time consumption.
Disentangled Self-Supervision in Sequential Recommenders
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.
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
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.
Understanding Negative Sampling in Graph Representation Learning
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.
Controllable Multi-Interest Framework for Recommendation
Recent works usually give an overall embedding from a user's behavior sequence.
Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities
To bridge this gap, we introduce a declustering based hidden variable model that leads to an efficient inference procedure via a variational autoencoder (VAE).
ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning
Motivated by this, we propose ExperienceThinking algorithm to quickly find the best possible hyperparameter configuration of machine learning algorithms within a few configuration evaluations.
Learning Disentangled Representations for Recommendation
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.
Dimensional Reweighting Graph Convolution Networks
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.
Dimensional Reweighting Graph Convolutional Networks
Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs.
Cognitive Knowledge Graph Reasoning for One-shot Relational Learning
Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently.
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
We propose a new CogQA framework for multi-hop question answering in web-scale documents.
Ranked #53 on
Question Answering
on HotpotQA
Multi-hop Question Answering
Multi-Hop Reading Comprehension
+1
Personalized Bundle List Recommendation
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.
AliGraph: A Comprehensive Graph Neural Network Platform
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
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
Ranked #1 on
Click-Through Rate Prediction
on Amazon Dataset
ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation
Downstream applications then can use the user behavior vectors via vanilla attention.
