Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Dabouei, Ali"'
Video Anomaly Detection (VAD) automates the identification of unusual events, such as security threats in surveillance videos. In real-world applications, VAD models must effectively operate in cross-domain settings, identifying rare anomalies and sc
Externí odkaz:
http://arxiv.org/abs/2408.05191
Autor:
Saadabadi, Mohammad Saeed Ebrahimi, Malakshan, Sahar Rahimi, Dabouei, Ali, Nasrabadi, Nasser M.
Aiming to enhance Face Recognition (FR) on Low-Quality (LQ) inputs, recent studies suggest incorporating synthetic LQ samples into training. Although promising, the quality factors that are considered in these works are general rather than FR-specifi
Externí odkaz:
http://arxiv.org/abs/2407.14972
Quality assessment of fingerprints captured using digital cameras and smartphones, also called fingerphotos, is a challenging problem in biometric recognition systems. As contactless biometric modalities are gaining more attention, their reliability
Externí odkaz:
http://arxiv.org/abs/2407.11141
Video anomaly detection aims to develop automated models capable of identifying abnormal events in surveillance videos. The benchmark setup for this task is extremely challenging due to: i) the limited size of the training sets, ii) weak supervision
Externí odkaz:
http://arxiv.org/abs/2406.02831
Autor:
Saadabadi, Mohammad Saeed Ebrahimi, Dabouei, Ali, Malakshan, Sahar Rahimi, Nasrabad, Nasser M.
Aiming to enhance the utilization of metric space by the parametric softmax classifier, recent studies suggest replacing it with a non-parametric alternative. Although a non-parametric classifier may provide better metric space utilization, it introd
Externí odkaz:
http://arxiv.org/abs/2403.16937
Autor:
Hakim, Zaber Ibn Abdul, Sarker, Najibul Haque, Singh, Rahul Pratap, Paul, Bishmoy, Dabouei, Ali, Xu, Min
A thorough comprehension of textual data is a fundamental element in multi-modal video analysis tasks. However, recent works have shown that the current models do not achieve a comprehensive understanding of the textual data during the training for t
Externí odkaz:
http://arxiv.org/abs/2312.06699
Limited data availability is a challenging problem in the latent fingerprint domain. Synthetically generated fingerprints are vital for training data-hungry neural network-based algorithms. Conventional methods distort clean fingerprints to generate
Externí odkaz:
http://arxiv.org/abs/2309.15734
Despite the fundamental distinction between adversarial and natural training (AT and NT), AT methods generally adopt momentum SGD (MSGD) for the outer optimization. This paper aims to analyze this choice by investigating the overlooked role of outer
Externí odkaz:
http://arxiv.org/abs/2209.01199
Autor:
Jeihouni, Paria, Dehzangi, Omid, Amireskandari, Annahita, Dabouei, Ali, Rezai, Ali, Nasrabadi, Nasser M.
Optical coherence tomography (OCT) is one of the non-invasive and easy-to-acquire biomarkers (the thickness of the retinal layers, which is detectable within OCT scans) being investigated to diagnose Alzheimer's disease (AD). This work aims to segmen
Externí odkaz:
http://arxiv.org/abs/2206.05277
Autor:
Soleymani, Sobhan, Dabouei, Ali, Taherkhani, Fariborz, Iranmanesh, Seyed Mehdi, Dawson, Jeremy, Nasrabadi, Nasser M.
We present a quality-aware multimodal recognition framework that combines representations from multiple biometric traits with varying quality and number of samples to achieve increased recognition accuracy by extracting complimentary identification i
Externí odkaz:
http://arxiv.org/abs/2112.05827