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pro vyhledávání: '"Zaheer Muhammad"'
Data augmentation is widely used to enhance generalization in visual classification tasks. However, traditional methods struggle when source and target domains differ, as in domain adaptation, due to their inability to address domain gaps. This paper
Externí odkaz:
http://arxiv.org/abs/2412.02366
Autor:
Khan, Asifullah, Sohail, Anabia, Fiaz, Mustansar, Hassan, Mehdi, Afridi, Tariq Habib, Marwat, Sibghat Ullah, Munir, Farzeen, Ali, Safdar, Naseem, Hannan, Zaheer, Muhammad Zaigham, Ali, Kamran, Sultana, Tangina, Tanoli, Ziaurrehman, Akhter, Naeem
Deep supervised learning models require high volume of labeled data to attain sufficiently good results. Although, the practice of gathering and annotating such big data is costly and laborious. Recently, the application of self supervised learning (
Externí odkaz:
http://arxiv.org/abs/2408.17059
Autor:
Saeed, Muhammad Saad, Nawaz, Shah, Zaheer, Muhammad Zaigham, Khan, Muhammad Haris, Nandakumar, Karthik, Yousaf, Muhammad Haroon, Sajjad, Hassan, De Schepper, Tom, Schedl, Markus
Multimodal networks have demonstrated remarkable performance improvements over their unimodal counterparts. Existing multimodal networks are designed in a multi-branch fashion that, due to the reliance on fusion strategies, exhibit deteriorated perfo
Externí odkaz:
http://arxiv.org/abs/2408.07445
Autor:
Liaqat, Muhammad Irzam, Nawaz, Shah, Zaheer, Muhammad Zaigham, Saeed, Muhammad Saad, Sajjad, Hassan, De Schepper, Tom, Nandakumar, Karthik, Schedl, Muhammad Haris Khan Markus
Multimodal learning has demonstrated remarkable performance improvements over unimodal architectures. However, multimodal learning methods often exhibit deteriorated performances if one or more modalities are missing. This may be attributed to the co
Externí odkaz:
http://arxiv.org/abs/2407.16243
Publikováno v:
Neural Computing and Applications, pp.1-17 (2024)
Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE
Externí odkaz:
http://arxiv.org/abs/2405.05886
Autor:
Saeed, Muhammad Saad, Nawaz, Shah, Tahir, Muhammad Salman, Das, Rohan Kumar, Zaheer, Muhammad Zaigham, Moscati, Marta, Schedl, Markus, Khan, Muhammad Haris, Nandakumar, Karthik, Yousaf, Muhammad Haroon
The advancements of technology have led to the use of multimodal systems in various real-world applications. Among them, the audio-visual systems are one of the widely used multimodal systems. In the recent years, associating face and voice of a pers
Externí odkaz:
http://arxiv.org/abs/2404.09342
Recently, a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques, two or more randomly selected natural images are mixed together to generate an augmented
Externí odkaz:
http://arxiv.org/abs/2405.14881
Autor:
Feng Rui, Hu Laigui, Zhang Youwei, Zaheer Muhammad, Qiu Zhi-Jun, Cong Chunxiao, Nie Qingmiao, Qin Yajie, Liu Ran
Publikováno v:
Nanophotonics, Vol 7, Iss 9, Pp 1563-1570 (2018)
Heterostructures with built-in electric fields are crucial for charge separation and lateral photovoltaic effect in current position-sensitive detectors (PSDs), which have to be produced by combining semiconductors with metal or other semiconductors
Externí odkaz:
https://doaj.org/article/781eef4bff214a689d143fdd2826ed09
Unsupervised (US) video anomaly detection (VAD) in surveillance applications is gaining more popularity recently due to its practical real-world applications. As surveillance videos are privacy sensitive and the availability of large-scale video data
Externí odkaz:
http://arxiv.org/abs/2404.00847
In order to devise an anomaly detection model using only normal training data, an autoencoder (AE) is typically trained to reconstruct the data. As a result, the AE can extract normal representations in its latent space. During test time, since AE is
Externí odkaz:
http://arxiv.org/abs/2403.16270