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Shafin Rahman

North South University, Bangladesh

24 papers hereh-index 212k citations61 works total

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

author position
  • first author6
  • middle author10
  • last author8

Across the 24 of 24 papers where every author was matched, so the position is known.

fields
  • cs.CV21
  • cs.AI1
  • cs.LG1
  • q-bio.QM1
affiliations
  • North South University, Bangladesh
same name
  • Shafin Rahman — 5 papers, h 5
  • Shafin Rahman — 2 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
20172023
most citedA Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning

182 citations · 305 across the 18 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.CV2018★ 30 cited

Task-generalizable Adversarial Attack based on Perceptual Metric

Muzammal Naseer, Salman H. Khan, Shafin Rahman +1

Deep neural networks (DNNs) can be easily fooled by adding human imperceptible perturbations to the images. These perturbed images are known as `adversarial examples' and pose a se…

cs.CV2018★ 45 cited

Polarity Loss for Zero-shot Object Detection

Shafin Rahman, Salman Khan, Nick Barnes

Conventional object detection models require large amounts of training data. In comparison, humans can recognize previously unseen objects by merely knowing their semantic descript…

cs.CV2018

Deep Multiple Instance Learning for Zero-shot Image Tagging

Shafin Rahman, Salman Khan

In-line with the success of deep learning on traditional recognition problem, several end-to-end deep models for zero-shot recognition have been proposed in the literature. These m…

cs.CV2018★ 1 cited

Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

Shafin Rahman, Salman Khan, Fatih Porikli

Current Zero-Shot Learning (ZSL) approaches are restricted to recognition of a single dominant unseen object category in a test image. We hypothesize that this setting is ill-suite…

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