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pro vyhledávání: '"Walia, Jaskaran Singh"'
SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers
Image classification is a computer vision task where a model analyzes an image to categorize it into a specific label. Vision Transformers (ViT) improve this task by leveraging self-attention to capture complex patterns and long range relationships b
Externí odkaz:
http://arxiv.org/abs/2411.09420
Large multilingual models have significantly advanced natural language processing (NLP) research. However, their high resource demands and potential biases from diverse data sources have raised concerns about their effectiveness across low-resource l
Externí odkaz:
http://arxiv.org/abs/2409.10965
Autor:
Walia, Jaskaran Singh, K, Pavithra L
Addressing the issue of submerged underwater trash is crucial for safeguarding aquatic ecosystems and preserving marine life. While identifying debris present on the surface of water bodies is straightforward, assessing the underwater submerged waste
Externí odkaz:
http://arxiv.org/abs/2405.18299
Publikováno v:
In Towards Autonomous Robotic Systems(2023) Springer Nature Switzerland; pages=292--303
Accurately quantifying and removing submerged underwater waste plays a crucial role in safeguarding marine life and preserving the environment. While detecting floating and surface debris is relatively straightforward, quantifying submerged waste pre
Externí odkaz:
http://arxiv.org/abs/2305.16460
Arrhythmia is just one of the many cardiovascular illnesses that have been extensively studied throughout the years. Using multi-lead ECG data, this research describes a deep learning (DL) pipeline technique based on convolutional neural network (CNN
Externí odkaz:
http://arxiv.org/abs/2303.03660
Several websites improve their security and avoid dangerous Internet attacks by implementing CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart), a type of verification to identify whether the end-user is human or a
Externí odkaz:
http://arxiv.org/abs/2302.09389