no code implementations • 10 Oct 2023 • Jiajun Zhang, Georgina Cosma, Sarah Bugby, Jason Watkins
Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or medical images by extracting features from the images, such as deep features, colour-based features, shape-based features and local features.
no code implementations • 16 Aug 2023 • Jingrui Hou, Georgina Cosma, Axel Finke
To address this challenge, a systematic task formulation of continual neural information retrieval is presented, along with a multiple-topic dataset that simulates continuous information retrieval.
no code implementations • 25 Jul 2023 • Jiajun Zhang, Georgina Cosma, Sarah Bugby, Jason Watkins
Additionally, this paper investigates the time performance of the FM toolkit when applied to four AI models with different datasets.
no code implementations • 21 Jul 2023 • Jiajun Zhang, Georgina Cosma, Sarah Bugby, Axel Finke, Jason Watkins
As the use of artificial intelligent (AI) models becomes more prevalent in industries such as engineering and manufacturing, it is essential that these models provide transparent reasoning behind their predictions.
no code implementations • 13 Jul 2023 • Eufrásio de A. Lima Neto, Jonathan Bailiss, Axel Finke, Jo Miller, Georgina Cosma
This paper investigates the utilisation of machine learning (ML) to assist experts in identifying families that may need to be referred for Early Help assessment and support.
no code implementations • 20 Apr 2023 • Andrew Houston, Georgina Cosma
Trust is a crucial factor affecting the adoption of machine learning (ML) models.
no code implementations • 13 Feb 2023 • Yan Gong, Georgina Cosma, Axel Finke
This paper introduces VITR, a novel network that enhances ViT by extracting and reasoning about image region relations based on a local encoder.
1 code implementation • 10 Oct 2022 • Yan Gong, Georgina Cosma
Visual Semantic Embedding (VSE) aims to extract the semantics of images and their descriptions, and embed them into the same latent space for cross-modal information retrieval.
1 code implementation • 4 Oct 2022 • Guoming Long, Tao Chen, Georgina Cosma
Yet, given the growing number of new GitHub reports for DL frameworks, it is intrinsically difficult for developers to distinguish those that reveal non-functional bugs among the others, and assign them to the right contributor for investigation in a timely manner.
1 code implementation • Neurocomputing 2020 • Aboozar Taherkhani, Georgina Cosma, T. M. McGinnity
AdaBoost-CNN is computationally efficient, as evidenced by the fact that the training simulation time of the proposed method is 47. 33 s, which is lower than the training simulation time required for a similar AdaBoost method without transfer learning, i. e. 225. 83 s on the imbalanced dataset.
no code implementations • 18 Sep 2019 • Pedro Machado, Georgina Cosma, T. M. McGinnity
Biological image processing is performed by complex neural networks composed of thousands of neurons interconnected via thousands of synapses, some of which are excitatory and others inhibitory.