most citedDetect-and-describe: Joint learning framework for detection and description of objects

5 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20221 cited

CNLL: A Semi-supervised Approach For Continual Noisy Label Learning

Nazmul Karim, Umar Khalid, Ashkan Esmaeili +1

The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario…

cs.CV20225 cited

Detect-and-describe: Joint learning framework for detection and description of objects

Addel Zafar, Umar Khalid

Traditional object detection answers two questions; "what" (what the object is?) and "where" (where the object is?). "what" part of the object detection can be fine-grained further…

eess.SP2022

RF Signal Transformation and Classification using Deep Neural Networks

Umar Khalid, Nazmul Karim, Nazanin Rahnavard

Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets. To address this chal…

cs.CV20211 cited

Adversarial Training for Face Recognition Systems using Contrastive Adversarial Learning and Triplet Loss Fine-tuning

Nazmul Karim, Umar Khalid, Nick Meeker +1

Though much work has been done in the domain of improving the adversarial robustness of facial recognition systems, a surprisingly small percentage of it has focused on self-superv…

cs.LG2021

Two-way Spectrum Pursuit for CUR Decomposition and Its Application in Joint Column/Row Subset Selection

Ashkan Esmaeili, Mohsen Joneidi, Mehrdad Salimitari +2

The problem of simultaneous column and row subset selection is addressed in this paper. The column space and row space of a matrix are spanned by its left and right singular vector…