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Found 11 papers, 3 papers with code
Efficient Retrieval of Images with Irregular Patterns using Morphological Image Analysis: Applications to Industrial and Healthcare datasets
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.
Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation
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.
ForestMonkey: Toolkit for Reasoning with AI-based Defect Detection and Classification Models
Additionally, this paper investigates the time performance of the FM toolkit when applied to four AI models with different datasets.
Morphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models
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.
Identifying Early Help Referrals For Local Authorities With Machine Learning And Bias Analysis
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.
A Meta-heuristic Approach to Estimate and Explain Classifier Uncertainty
Trust is a crucial factor affecting the adoption of machine learning (ML) models.
VITR: Augmenting Vision Transformers with Relation-Focused Learning for Cross-Modal Information Retrieval
This paper introduces VITR, a novel network that enhances ViT by extracting and reasoning about image region relations based on a local encoder.
Improving Visual-Semantic Embeddings by Learning Semantically-Enhanced Hard Negatives for Cross-modal Information Retrieval
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.
Multifaceted Hierarchical Report Identification for Non-Functional Bugs in Deep Learning Frameworks
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.
AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning
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.
NatCSNN: A Convolutional Spiking Neural Network for recognition of objects extracted from natural images
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.
