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V. Chandrasekhar

37 papers hereh-index 418.3k citations142 works total

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

author position
  • first author2
  • middle author19
  • last author15

Across the 36 of 37 papers where every author was matched, so the position is known.

fields
  • cs.CV16
  • cs.LG10
  • cs.CR2
  • cs.NE2
  • cond-mat.mtrl-sci1
  • cs.AI1
same name
  • V. Chandrasekhar — 45 papers, h 22
  • V. Chandrasekhar — 21 papers, h 8
  • V. Chandrasekhar — 8 papers, h 20
  • V. Chandrasekhar — 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
20152021
most citedEfficient GAN-Based Anomaly Detection

482 citations · 1.4k across the 36 of their papers we have counts for

collaborators
Showing 2015Show all

4 papers · 1 filter

cs.IR2015★ 6 cited

Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing

Jie Lin, Olivier Morère, Julie Petta +2

A typical image retrieval pipeline starts with the comparison of global descriptors from a large database to find a short list of candidate matches. A good image descriptor is key…

cs.CV2015★ 12 cited

A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval

Vijay Chandrasekhar, Jie Lin, Olivier Morère +2

With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher…

cs.CV2015★ 5 cited

Co-Regularized Deep Representations for Video Summarization

Olivier Morère, Hanlin Goh, Antoine Veillard +2

Compact keyframe-based video summaries are a popular way of generating viewership on video sharing platforms. Yet, creating relevant and compelling summaries for arbitrarily long v…

cs.CV2015★ 27 cited

DeepHash: Getting Regularization, Depth and Fine-Tuning Right

Jie Lin, Olivier Morere, Vijay Chandrasekhar +2

This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval. We propose DeepHash: a hashing…

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