no code implementations • 27 Sep 2023 • Javed Iqbal, Aliza Masood, Waqas Sultani, Mohsen Ali
In this work, we propose a topology-aware unsupervised domain adaptation approach for road segmentation in remote sensing imagery.
no code implementations • 18 Aug 2023 • Xiaohan Zhang, Xingyu Li, Waqas Sultani, Chen Chen, Safwan Wshah
We attribute this deficiency to the lack of ability to extract the geometric layout of visual features and models' overfitting to low-level details.
no code implementations • 11 Aug 2023 • Muhammad Atif Butt, Hassan Ali, Adnan Qayyum, Waqas Sultani, Ala Al-Fuqaha, Junaid Qadir
Semantic understanding of roadways is a key enabling factor for safe autonomous driving.
1 code implementation • 8 Dec 2022 • Xiaohan Zhang, Xingyu Li, Waqas Sultani, Yi Zhou, Safwan Wshah
We attribute this deficiency to the lack of ability to extract the spatial configuration of visual feature layouts and models' overfitting on low-level details from the training set.
1 code implementation • 25 Oct 2022 • Xiaohan Zhang, Waqas Sultani, Safwan Wshah
In this paper, we present the first cross-view geo-localization method that works on a sequence of limited FOV images.
3 code implementations • 16 Oct 2022 • Tushar Sangam, Ishan Rajendrakumar Dave, Waqas Sultani, Mubarak Shah
Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones.
no code implementations • 9 Apr 2022 • M. Fasi ur Rehman, Izza Ali, Waqas Sultani, Mohsen Ali
A small set of seed samples (32 in our case) are automatically discovered by analyzing the temporal changes, which are manually labeled to train a segmentation and representation learning module.
no code implementations • 30 Dec 2021 • Daniel Wilson, Xiaohan Zhang, Waqas Sultani, Safwan Wshah
The concept of geo-localization refers to the process of determining where on earth some `entity' is located, typically using Global Positioning System (GPS) coordinates.
no code implementations • CVPR 2022 • Waqas Sultani, Wajahat Nawaz, Syed Javed, Muhammad Sohail Danish, Asma Saadia, Mohsen Ali
We design a mechanism to transfer these annotations from the high-cost microscope at high magnification to the low-cost microscope, at multiple magnifications.
no code implementations • 2 Nov 2021 • Muhammad Zaida, Shafaqat Ali, Mohsen Ali, Sarfaraz Hussein, Asma Saadia, Waqas Sultani
Deep neural networks have shown promising results in disease detection and classification using medical image data.
1 code implementation • 26 Oct 2021 • Muaaz Zakria, Hamza Rawal, Waqas Sultani, Mohsen Ali
We then exploit counting consistency constraints, within-image count consistency, and across-image count consistency, to decrease the domain shift.
no code implementations • 10 Apr 2021 • Nadeem Yousaf, Sarfaraz Hussein, Waqas Sultani
The recent works have either employed hand-crafted geometrical face features or face-level deep convolutional neural network features for face to BMI prediction.
2 code implementations • CVPR 2021 • Muhammad Waseem Ashraf, Waqas Sultani, Mubarak Shah
The erratic movement of the source and target drones, small size, arbitrary shape, large intensity variations, and occlusion make this problem quite challenging.
no code implementations • 17 Feb 2021 • Qazi Ammar Arshad, Mohsen Ali, Saeed-Ul Hassan, Chen Chen, Ayisha Imran, Ghulam Rasul, Waqas Sultani
Malaria microscopy, microscopic examination of stained blood slides to detect parasite Plasmodium, is considered to be a gold-standard for detecting life-threatening disease malaria.
no code implementations • 3 Jan 2021 • Bilal Yousaf, Muhammad Usama, Waqas Sultani, Arif Mahmood, Junaid Qadir
The proposed detector has demonstrated significant performance improvement compared to the current state-of-the-art fake content detectors and fusing the frequency and spatial domain streams has also improved generalization of the detector.
no code implementations • 22 Apr 2020 • Waqas Sultani, Qazi Ammar Arshad, Chen Chen
Temporal localization (i. e. indicating the start and end frames of the action in a video) is referred to as frame-level detection.
no code implementations • 1 Apr 2020 • Sijie Zhu, Chen Chen, Waqas Sultani
Temporal localization (i. e. indicating the start and end frames of the anomaly event in a video) is referred to as frame-level detection.
1 code implementation • ISPRS Journal of Photogrammetry and Remote Sensing 2020 • Muhammad Usman Ali, Waqas Sultani, Mohsen Ali
Natural and man-made disasters cause huge damage to built infrastructures and results in loss of human lives.
no code implementations • 22 Oct 2019 • Waqas Sultani, Mubarak Shah
However, using deep neural networks for automatic aerial action recognition is difficult due to the need for a large number of training aerial human action videos.
no code implementations • 1 Apr 2019 • Anza Shakeel, Waqas Sultani, Mohsen Ali
In this paper, we attempt to address the challenging problem of counting built-structures in the satellite imagery.
8 code implementations • CVPR 2018 • Waqas Sultani, Chen Chen, Mubarak Shah
To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.
Ranked #2 on Abnormal Event Detection In Video on UBI-Fights
Activity Recognition Anomaly Detection In Surveillance Videos +2
no code implementations • 3 Apr 2017 • Waqas Sultani, Dong Zhang, Mubarak Shah
Given the action proposals in a video, the goal of the proposed work is to generate a few better action proposals that are ranked properly.
no code implementations • CVPR 2016 • Waqas Sultani, Mubarak Shah
%We reconstruct video action proposals from image action proposals while enforcing consistency across coefficient vectors of multiple frames by consensus regularization.
Optical Flow Estimation Spatio-Temporal Action Localization +1
no code implementations • 26 May 2016 • Waqas Sultani, Mubarak Shah
The output of our method is the most action representative proposals from each video.
no code implementations • CVPR 2014 • Waqas Sultani, Imran Saleemi
This paper attempts to address the problem of recognizing human actions while training and testing on distinct datasets, when test videos are neither labeled nor available during training.
Ranked #2 on Domain Adaptation on UCF-to-Olympic