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Licheng Yu

Microsoft

13 papers hereh-index 259.8k citations43 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author10

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • cs.CL4
affiliations
  • Microsoft
  • University of North Carolina at Chapel Hill
Homepage
same name
  • Licheng Yu — 8 papers
  • Licheng Yu — 8 papers, h 12
  • Licheng Yu — 8 papers, h 7
  • Licheng Yu — 4 papers, h 4
  • Licheng Yu — 2 papers, h 2
  • Licheng Yu — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152020
most citedVisual Madlibs: Fill in the blank Image Generation and Question Answering

80 citations · 162 across the 6 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2020

What is More Likely to Happen Next? Video-and-Language Future Event Prediction

Jie Lei, Licheng Yu, Tamara L. Berg +1

Given a video with aligned dialogue, people can often infer what is more likely to happen next. Making such predictions requires not only a deep understanding of the rich dynamics…

cs.CL2019

Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout

Hao Tan, Licheng Yu, Mohit Bansal

A grand goal in AI is to build a robot that can accurately navigate based on natural language instructions, which requires the agent to perceive the scene, understand and ground la…

cs.CL2018

TVQA: Localized, Compositional Video Question Answering

Jie Lei, Licheng Yu, Mohit Bansal +1

Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks. However, due to data limitations, there has been much less work on video-based QA.…

cs.CL2017★ 9 cited

Hierarchically-Attentive RNN for Album Summarization and Storytelling

Licheng Yu, Mohit Bansal, Tamara L. Berg

We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural languag…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.