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Liangliang Cao

40 papers hereh-index 427.9k citations155 works total

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

author position
  • first author2
  • middle author30
  • last author7

Across the 39 of 40 papers where every author was matched, so the position is known.

fields
  • cs.CV18
  • eess.AS9
  • cs.CL5
  • cs.DC2
  • eess.IV2
  • cs.AI1
same name
  • Liangliang Cao — 8 papers, h 5
  • Liangliang Cao — 3 papers, h 3
  • Liangliang Cao — 2 papers, h 2
  • Liangliang Cao — 1 paper, h 1
  • Liangliang Cao — 1 paper, h 0
  • Liangliang Cao — 1 paper, 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 citedMining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data

177 citations · 826 across the 39 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017★ 27 cited

Improving Object Detection from Scratch via Gated Feature Reuse

Zhiqiang Shen, Honghui Shi, Jiahui Yu +7

In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i.e., without pre-trained models). We…

cs.CV2017

Lip2AudSpec: Speech reconstruction from silent lip movements video

Hassan Akbari, Himani Arora, Liangliang Cao +1

In this study, we propose a deep neural network for reconstructing intelligible speech from silent lip movement videos. We use auditory spectrogram as spectral representation of sp…

cs.CV2017★ 23 cited

MemexQA: Visual Memex Question Answering

Lu Jiang, Junwei Liang, Liangliang Cao +3

This paper proposes a new task, MemexQA: given a collection of photos or videos from a user, the goal is to automatically answer questions that help users recover their memory abou…

cs.CV2017★ 58 cited

Learning from Noisy Labels with Distillation

Yuncheng Li, Jianchao Yang, Yale Song +3

The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally,…

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