activity
20172022
most citedSynergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection

7 citations · 14 across the 9 of their papers we have counts for

collaborators

12 papers

eess.IV2021

Out of distribution detection for skin and malaria images

Muhammad Zaida, Shafaqat Ali, Mohsen Ali +3

Deep neural networks have shown promising results in disease detection and classification using medical image data. However, they still suffer from the challenges of handling real-…

cs.CV20217 cited

Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection

Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz +1

We study adapting trained object detectors to unseen domains manifesting significant variations of object appearance, viewpoints and backgrounds. Most current methods align domains…

eess.IV2021

A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images

Qazi Ammar Arshad, Mohsen Ali, Saeed-ul Hassan +4

Malaria microscopy, microscopic examination of stained blood slides to detect parasite Plasmodium, is considered to be a gold-standard for detecting life-threatening disease malari…

cs.CV2020

Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation

M. Naseer Subhani, Mohsen Ali

Self-supervised learning approaches for unsupervised domain adaptation (UDA) of semantic segmentation models suffer from challenges of predicting and selecting reasonable good qual…

cs.CV2020

Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery

Javed Iqbal, Mohsen Ali

This paper proposes a novel domain adaptation algorithm to handle the challenges posed by the satellite and aerial imagery, and demonstrates its effectiveness on the built-up regio…

cs.CV2020

Localizing Firearm Carriers by Identifying Human-Object Pairs

Abdul Basit, Muhammad Akhtar Munir, Mohsen Ali +1

Visual identification of gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm). We present a novel approach to ad…