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Subhransu Maji

University of Massachusetts Amherst

65 papers hereh-index 5026.9k citations149 works total

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

author position
  • sole author1
  • first author1
  • middle author35
  • last author28

Across the 65 of 65 papers where every author was matched, so the position is known.

fields
  • cs.CV56
  • cs.LG4
  • astro-ph.GA1
  • cs.CL1
  • cs.GR1
  • eess.IV1
affiliations
  • University of Massachusetts Amherst
Homepage
same name
  • Subhransu Maji — 34 papers, h 7

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
20112023
most citedFine-Grained Visual Classification of Aircraft

1.2k citations · 2k across the 56 of their papers we have counts for

collaborators
Showing 2018 · cs.CVShow all

4 papers · 2 filters

cs.CV2018★ 1 cited

A Deeper Look at 3D Shape Classifiers

Jong-Chyi Su, Matheus Gadelha, Rui Wang +1

We investigate the role of representations and architectures for classifying 3D shapes in terms of their computational efficiency, generalization, and robustness to adversarial tra…

cs.CV2018★ 3 cited

Second-order Democratic Aggregation

Tsung-Yu Lin, Subhransu Maji, Piotr Koniusz

Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classificatio…

cs.CV2018★ 41 cited

Multiresolution Tree Networks for 3D Point Cloud Processing

Matheus Gadelha, Rui Wang, Subhransu Maji

We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks. Our network represents a 3D shape as a set of locality-…

cs.CV2018★ 73 cited

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

Hang Su, Varun Jampani, Deqing Sun +4

We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.…

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