no code implementations • CVPR 2023 • Burak Uzkent, Amanmeet Garg, Wentao Zhu, Keval Doshi, Jingru Yi, Xiaolong Wang, Mohamed Omar
For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks.
no code implementations • 7 Apr 2022 • Furkan Mumcu, Keval Doshi, Yasin Yilmaz
We demonstrate how Wi-Fi deauthentication attack, a notoriously easy-to-perform and effective denial-of-service (DoS) attack, can be utilized to generate adversarial data for video anomaly detection systems.
1 code implementation • 10 Mar 2022 • Keval Doshi, Yasin Yilmaz
While video action recognition has been an active area of research for several years, zero-shot action recognition has only recently started gaining traction.
1 code implementation • 10 Mar 2022 • Keval Doshi, Shatha Abudalou, Yasin Yilmaz
While anomaly detection in time series has been an active area of research for several years, most recent approaches employ an inadequate evaluation criterion leading to an inflated F1 score.
no code implementations • WACV 2022 • Keval Doshi, Yasin Yilmaz
The experimental results show that the existing state-of-the-art methods are not suitable for the considered practical challenges, and the proposed algorithm outperforms them with a large margin in continual learning and few-shot learning tasks
no code implementations • 20 Apr 2021 • Keval Doshi, Yasin Yilmaz
We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic.
no code implementations • 21 Mar 2021 • Keval Doshi, Yasin Yilmaz
Anomaly detection in videos has been attracting an increasing amount of attention.
1 code implementation • 30 Oct 2020 • Keval Doshi, Yasin Yilmaz
Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors.
1 code implementation • 10 Oct 2020 • Keval Doshi, Yasin Yilmaz
Motivated by these research gaps, we propose an online anomaly detection method in surveillance videos with asymptotic bounds on the false alarm rate, which in turn provides a clear procedure for selecting a proper decision threshold that satisfies the desired false alarm rate.
1 code implementation • CVPR 2020 • Keval Doshi, Yasin Yilmaz
In this paper, we propose a fast unsupervised anomaly detection system comprising of three modules: preprocessing module, candidate selection module and backtracking anomaly detection module.
no code implementations • 15 Jun 2020 • Keval Doshi, Yasin Yilmaz, Suleyman Uludag
Internet of Things (IoT) networks consist of sensors, actuators, mobile and wearable devices that can connect to the Internet.
no code implementations • 15 Apr 2020 • Keval Doshi, Yasin Yilmaz
Anomaly detection in surveillance videos has been recently gaining attention.
Anomaly Detection In Surveillance Videos Continual Learning +2
no code implementations • 5 Apr 2020 • Keval Doshi, Yasin Yilmaz
Even though the performance of state-of-the-art methods on publicly available data sets has been competitive, they demand a massive amount of training data.
Ranked #21 on Anomaly Detection on ShanghaiTech
no code implementations • 31 Jul 2019 • Keval Doshi
Object detection and recognition has been an ongoing research topic for a long time in the field of computer vision.