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Ruiming Cao

UC Berkeley

5 papers hereh-index 12814 citations24 works total

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

author position
  • first author1
  • middle author4

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • eess.IV1
affiliations
  • UC Berkeley
Homepage
same name
  • Ruiming Cao — 5 papers, h 3
  • Ruiming Cao — 1 paper

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
20172019
most citedInterpreting CNN Knowledge via an Explanatory Graph

33 citations · 54 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2018★ 2 cited

Explanatory Graphs for CNNs

Quanshi Zhang, Xin Wang, Ruiming Cao +3

This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN. Each filter in a conv-la…

cs.CV2018★ 2 cited

Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering

Quanshi Zhang, Ruiming Cao, Ying Nian Wu +1

In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized b…

cs.CV2017★ 33 cited

Interpreting CNN Knowledge via an Explanatory Graph

Quanshi Zhang, Ruiming Cao, Feng Shi +2

This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv-laye…

cs.CV2017★ 17 cited

Interactively Transferring CNN Patterns for Part Localization

Quanshi Zhang, Ruiming Cao, Shengming Zhang +3

In the scenario of one/multi-shot learning, conventional end-to-end learning strategies without sufficient supervision are usually not powerful enough to learn correct patterns fro…

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