A multi-class partial hinge loss for partial label learning

Fan, Jinfu; Jiang, Zhencun; Xian, Yuanqing; Wang, Zhongjie

doi:10.1007/s10489-023-04954-1

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…The strategy laid out within the strategy portrayal for the urban thing acknowledgement venture is an imaginative and careful approach particularly planned for circumstances that involve cityscapes. The term paper presents an inventive system called a dual-generator generative Ill-disposed arrange (GAN), which empowers bidirectional interpretation between DC and NTIR pictures [17]. This procedure increases pseudo labels with an inline tag clarification module.…”

Section: Methods Descriptionmentioning

confidence: 99%

Comparing hybrid models for recognising objects in thermal images at nighttime

Bandi,

Rajakumari

2024

IJEECS

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This research aims to revolutionize urban object recognition by developing cloud-based Python programs using intelligent algorithms. Unlike current models that focus on colour enhancement in nighttime thermal images, this work addresses the critical challenge of accurate object detection in urban landscapes. The proposed method incorporates a binary generative adversarial network (GAN) generator that can switch bidirectionally between daytime colour (DC) and nighttime infrared (NTIR) images. memory-based visual image memory (MVAM), system extracts important descriptive information from urban landscape images, reducing problems related to small sample sizes. This discussion presents a comprehensive improvement and evaluation of a deep learning image classification pipeline using Google Colab, demonstrating advanced image processing. Using TensorFlow, Keres and scikit image libraries combined with advanced algorithms such as DenseNet121 and MobileNetV2 presents a clear approach. We created a Bidirectional GAN + MVAM for object recognition in this work. Our method performed well, with an accuracy of 81.43%, precision of 51.16, recall of 50.11, and F-score of 46.37. The systematic presentation of the code presents a careful strategy to ensure optimal performance, stability, and efficiency of deep learning and image processing tasks.

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Section: Methods Descriptionmentioning

confidence: 99%