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Yangyan Li

13 papers hereh-index 226.6k citations36 works total

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

author position
  • first author1
  • middle author8
  • last author3

Across the 12 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CV12
  • cs.RO1
same name
  • Yangyan Li — 5 papers, h 3
  • Yangyan Li — 1 paper, h 1

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
20152024
most citedRender for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views

115 citations · 218 across the 9 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020

GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling

Zhuoying Wang, Yongtao Wang, Zhi Tang +4

Existing CNN-based methods for pixel labeling heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation. State-of-the-a…

cs.CV2020★ 40 cited

DO-Conv: Depthwise Over-parameterized Convolutional Layer

Jinming Cao, Yangyan Li, Mingchao Sun +5

Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise c…

cs.CV2020

Face Identity Disentanglement via Latent Space Mapping

Yotam Nitzan, Amit Bermano, Yangyan Li +1

Learning disentangled representations of data is a fundamental problem in artificial intelligence. Specifically, disentangled latent representations allow generative models to cont…

cs.CV2020★ 1 cited

MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recogntion

Kaiyu Shan, Yongtao Wang, Zhuoying Wang +4

To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-art methods integrate 1D temporal convolution into a conventional 2D CNN backbone.…

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