◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Shujon Naha

3 papers hereh-index 9416 citations16 works total

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

author position
  • middle author2

Across the 2 of 3 papers where every author was matched, so the position is known.

fields
  • cs.CV3
same name
  • Shujon Naha — 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
20172020
most citedLabel Refinement Network for Coarse-to-Fine Semantic Segmentation

51 citations · 51 across the 1 of their papers we have counts for

collaborators

3 papers

cs.CV2020

HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation

Bardia Doosti, Shujon Naha, Majid Mirbagheri +1

Hand-object pose estimation (HOPE) aims to jointly detect the poses of both a hand and of a held object. In this paper, we propose a lightweight model called HOPE-Net which jointly…

cs.CV2018

Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling

Md Amirul Islam, Mrigank Rochan, Shujon Naha +2

Effective integration of local and global contextual information is crucial for semantic segmentation and dense image labeling. We develop two encoder-decoder based deep learning a…

cs.CV2017★ 51 cited

Label Refinement Network for Coarse-to-Fine Semantic Segmentation

Md Amirul Islam, Shujon Naha, Mrigank Rochan +2

We consider the problem of semantic image segmentation using deep convolutional neural networks. We propose a novel network architecture called the label refinement network that pr…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.