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Hang Li

19 papers hereh-index 172.9k citations24 works total

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

author position
  • first author1
  • middle author10
  • last author7

Across the 18 of 19 papers where every author was matched, so the position is known.

fields
  • cs.CL8
  • cs.CV5
  • cs.LG4
  • cs.AI1
  • cs.IR1
same name
  • Hang Li — 31 papers, h 55
  • Hang Li — 24 papers, h 18
  • Hang Li — 21 papers, h 17
  • Hang Li — 18 papers, h 18
  • Hang Li — 10 papers, h 7
  • Hang Li — 10 papers, h 14

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
20152023
most citedMeta-SGD: Learning to Learn Quickly for Few-Shot Learning

849 citations · 1.1k across the 19 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CL2021★ 97 cited

Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts

Yan Zeng, Xinsong Zhang, Hang Li

Most existing methods in vision language pre-training rely on object-centric features extracted through object detection and make fine-grained alignments between the extracted feat…

cs.CV2021★ 6 cited

Self-Supervised Learning by Estimating Twin Class Distributions

Feng Wang, Tao Kong, Rufeng Zhang +2

We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We emplo…

cs.AI2021★ 2 cited

The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding

Volker Tresp, Sahand Sharifzadeh, Hang Li +2

We present a unified computational theory of an agent's perception and memory. In our model, perception, episodic memory, and semantic memory are realized by different operational…

cs.CV2021

Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering

Rajat Koner, Hang Li, Marcel Hildebrandt +3

Visual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and t…

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