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Lizhen Qu

24 papers hereh-index 253.6k citations53 works total

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

author position
  • first author3
  • middle author17
  • last author3

Across the 23 of 24 papers where every author was matched, so the position is known.

fields
  • cs.CL14
  • cs.CV4
  • cs.LG4
  • cs.AI1
  • stat.ML1
same name
  • Lizhen Qu — 35 papers, h 13
  • Lizhen Qu — 11 papers, h 4
  • Lizhen Qu — 8 papers, h 3
  • Lizhen Qu — 4 papers, h 2
  • Lizhen Qu — 2 papers, h 3
  • Lizhen Qu — 2 papers, h 2

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
20142023
most citedMaking Deep Neural Networks Robust to Label Noise: a Loss Correction Approach

114 citations · 390 across the 23 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2022★ 36 cited

Learning Object-Language Alignments for Open-Vocabulary Object Detection

Chuang Lin, Peize Sun, Yi Jiang +5

Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding…

cs.CV2022

ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities

Terry Yue Zhuo, Yaqing Liao, Yuecheng Lei +5

We introduce ViLPAct, a novel vision-language benchmark for human activity planning. It is designed for a task where embodied AI agents can reason and forecast future actions of hu…

cs.CV2021★ 1 cited

Multimodal Transformer with Variable-length Memory for Vision-and-Language Navigation

Chuang Lin, Yi Jiang, Jianfei Cai +3

Vision-and-Language Navigation (VLN) is a task that an agent is required to follow a language instruction to navigate to the goal position, which relies on the ongoing interactions…

cs.CV2016★ 11 cited

Automatic Generation of Grounded Visual Questions

Shijie Zhang, Lizhen Qu, Shaodi You +2

In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image. Visual question generation is an emerging topic…

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