Linjie Li

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Purdue University ∙ The Regents of the University of California

VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation

Most existing video-and-language (VidL) research focuses on a single dat...
06/08/2021 ∙ by Linjie Li, et al. ∙ 3 ∙ share

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Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models

With large-scale pre-training, the past two years have witnessed signifi...
06/01/2021 ∙ by Linjie Li, et al. ∙ 22 ∙ share

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Playing Lottery Tickets with Vision and Language

Large-scale transformer-based pre-training has recently revolutionized v...
04/23/2021 ∙ by Zhe Gan, et al. ∙ 11 ∙ share

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UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

Vision-and-language pre-training has achieved impressive success in lear...
04/01/2021 ∙ by Mingyang Zhou, et al. ∙ 2 ∙ share

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LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval

Multimodal pre-training has propelled great advancement in vision-and-la...
03/16/2021 ∙ by Siqi Sun, et al. ∙ 13 ∙ share

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Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling

The canonical approach to video-and-language learning (e.g., video quest...
02/11/2021 ∙ by Jie Lei, et al. ∙ 2 ∙ share

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A Closer Look at the Robustness of Vision-and-Language Pre-trained Models

Large-scale pre-trained multimodal transformers, such as ViLBERT and UNI...
12/15/2020 ∙ by Linjie Li, et al. ∙ 1 ∙ share

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Graph Optimal Transport for Cross-Domain Alignment

Cross-domain alignment between two sets of entities (e.g., objects in an...
06/26/2020 ∙ by Liqun Chen, et al. ∙ 9 ∙ share

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Large-Scale Adversarial Training for Vision-and-Language Representation Learning

We present VILLA, the first known effort on large-scale adversarial trai...
06/11/2020 ∙ by Zhe Gan, et al. ∙ 6 ∙ share

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HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

We present HERO, a Hierarchical EncodeR for Omni-representation learning...
05/01/2020 ∙ by Linjie Li, et al. ∙ 3 ∙ share

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Meta Module Network for Compositional Visual Reasoning

There are two main lines of research on visual reasoning: neural module ...
10/08/2019 ∙ by Wenhu Chen, et al. ∙ 0 ∙ share

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UNITER: Learning UNiversal Image-TExt Representations

Joint image-text embedding is the bedrock for most Vision-and-Language (...
09/25/2019 ∙ by Yen-Chun Chen, et al. ∙ 0 ∙ share

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Relation-aware Graph Attention Network for Visual Question Answering

In order to answer semantically-complicated questions about an image, a ...
03/29/2019 ∙ by Linjie Li, et al. ∙ 0 ∙ share

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Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog

This paper presents Recurrent Dual Attention Network (ReDAN) for visual ...
02/01/2019 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

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Learning to see people like people

Humans make complex inferences on faces, ranging from objective properti...
05/05/2017 ∙ by Amanda Song, et al. ∙ 0 ∙ share

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Linjie Li

Featured Co-authors

Lawrence Carin

Jianfeng Gao

Mohit Bansal

William Yang Wang

Yu Cheng

Zhe Gan

Zhou Yu

Jingjing Liu

Tianlong Chen

Chen Zhu

Wenhu Chen

VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation

Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models

Playing Lottery Tickets with Vision and Language

UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval

Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling

A Closer Look at the Robustness of Vision-and-Language Pre-trained Models

Graph Optimal Transport for Cross-Domain Alignment

Large-Scale Adversarial Training for Vision-and-Language Representation Learning

HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

Meta Module Network for Compositional Visual Reasoning

UNITER: Learning UNiversal Image-TExt Representations

Relation-aware Graph Attention Network for Visual Question Answering

Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog

Learning to see people like people