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Jiashi Li

16 papers hereh-index 131.4k citations20 works total

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

author position
  • first author1
  • middle author13

Across the 14 of 16 papers where every author was matched, so the position is known.

fields
  • cs.CV12
  • cs.DC2
  • cs.AI1
  • eess.IV1
same name
  • Jiashi Li — 15 papers, h 10
  • Jiashi Li — 1 paper, h 2
  • Jiashi 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
20212026
most citedNext-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

138 citations · 216 across the 16 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CV2022★ 2 cited

Multi-Objective Evolutionary for Object Detection Mobile Architectures Search

Haichao Zhang, Jiashi Li, Xin Xia +2

Recently, Neural architecture search has achieved great success on classification tasks for mobile devices. The backbone network for object detection is usually obtained on the ima…

cs.CV2022★ 138 cited

Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Jiashi Li, Xin Xia, Wei Li +6

Due to the complex attention mechanisms and model design, most existing vision Transformers (ViTs) can not perform as efficiently as convolutional neural networks (CNNs) in realist…

cs.CV2022★ 14 cited

MoCoViT: Mobile Convolutional Vision Transformer

Hailong Ma, Xin Xia, Xing Wang +3

Recently, Transformer networks have achieved impressive results on a variety of vision tasks. However, most of them are computationally expensive and not suitable for real-world mo…

cs.CV2022★ 19 cited

TRT-ViT: TensorRT-oriented Vision Transformer

Xin Xia, Jiashi Li, Jie Wu +4

We revisit the existing excellent Transformers from the perspective of practical application. Most of them are not even as efficient as the basic ResNets series and deviate from th…

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