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pro vyhledávání: '"Chasmai, Mustafa"'
Increasing attention is being diverted to data-efficient problem settings like Open Vocabulary Semantic Segmentation (OVSS) which deals with segmenting an arbitrary object that may or may not be seen during training. The closest standard problems rel
Externí odkaz:
http://arxiv.org/abs/2302.14163
Autor:
Baby, Britty, Thapar, Daksh, Chasmai, Mustafa, Banerjee, Tamajit, Dargan, Kunal, Suri, Ashish, Banerjee, Subhashis, Arora, Chetan
Minimally invasive surgeries and related applications demand surgical tool classification and segmentation at the instance level. Surgical tools are similar in appearance and are long, thin, and handled at an angle. The fine-tuning of state-of-the-ar
Externí odkaz:
http://arxiv.org/abs/2211.16200
Few-shot Learning (FSL) methods are being adopted in settings where data is not abundantly available. This is especially seen in medical domains where the annotations are expensive to obtain. Deep Neural Networks have been shown to be vulnerable to a
Externí odkaz:
http://arxiv.org/abs/2210.03429
Despite the tremendous progress made by deep learning models in image semantic segmentation, they typically require large annotated examples, and increasing attention is being diverted to problem settings like Few-Shot Learning (FSL) where only a sma
Externí odkaz:
http://arxiv.org/abs/2208.12428
Yoga is a globally acclaimed and widely recommended practice for a healthy living. Maintaining correct posture while performing a Yogasana is of utmost importance. In this work, we employ transfer learning from Human Pose Estimation models for extrac
Externí odkaz:
http://arxiv.org/abs/2206.13577
Person Re-Identification (Re-ID) is an important problem in computer vision-based surveillance applications, in which one aims to identify a person across different surveillance photographs taken from different cameras having varying orientations and
Externí odkaz:
http://arxiv.org/abs/2204.13158
Autor:
Chasmai, Mustafa Ebrahim
Since its first appearance, transformers have been successfully used in wide ranging domains from computer vision to natural language processing. Application of transformers in Reinforcement Learning by reformulating it as a sequence modelling proble
Externí odkaz:
http://arxiv.org/abs/2111.06036
Autor:
Lu, Haiping, Liu, Xianyuan, Turner, Robert, Bai, Peizhen, Koot, Raivo E, Zhou, Shuo, Chasmai, Mustafa, Schobs, Lawrence
Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems. However, significant barriers exist in crossing disciplinary boundaries when most machine learning tools are developed in different areas
Externí odkaz:
http://arxiv.org/abs/2106.09756
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