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Junlan Feng

27 papers hereh-index 17759 citations68 works total

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

author position
  • first author1
  • middle author15
  • last author11

Across the 27 of 27 papers where every author was matched, so the position is known.

fields
  • cs.CL14
  • cs.CV4
  • cs.LG3
  • cs.NI3
  • cs.AI1
  • cs.IR1
same name
  • Junlan Feng — 17 papers, h 15
  • Junlan Feng — 16 papers, h 7
  • Junlan Feng — 13 papers, h 3
  • Junlan Feng — 12 papers, h 3
  • Junlan Feng — 10 papers, h 1
  • Junlan Feng — 8 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
20202026
most citedNetwork Topology Optimization via Deep Reinforcement Learning

3 citations · 12 across the 27 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition

Yulin Wang, Haoji Zhang, Yang Yue +4

This paper presents a comprehensive exploration of the phenomenon of data redundancy in video understanding, with the aim to improve computational efficiency. Our investigation com…

cs.CV2024

Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition

Ruizhuo Xu, Ke Wang, Chao Deng +5

With the increasing availability of consumer depth sensors, 3D face recognition (FR) has attracted more and more attention. However, the data acquired by these sensors are often co…

cs.CV2023★ 1 cited

Fine-grained Recognition with Learnable Semantic Data Augmentation

Yifan Pu, Yizeng Han, Yulin Wang +3

Fine-grained image recognition is a longstanding computer vision challenge that focuses on differentiating objects belonging to multiple subordinate categories within the same meta…

cs.CV2023★ 2 cited

Dynamic Perceiver for Efficient Visual Recognition

Yizeng Han, Dongchen Han, Zeyu Liu +7

Early exiting has become a promising approach to improving the inference efficiency of deep networks. By structuring models with multiple classifiers (exits), predictions for ``eas…

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