AIMC Topic: Semantics

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Semantic information-based attention mapping network for few-shot knowledge graph completion.

Neural networks : the official journal of the International Neural Network Society
Few-shot Knowledge Graph Completion (FKGC), an emerging technology capable of inferring new triples using only a few reference relation triples, has gained significant attention in recent years. However, existing FKGC methods primarily focus on struc...

Dynamic semantic-geometric guidance and structure transfer network for cross-scene hyperspectral image classification.

Neural networks : the official journal of the International Neural Network Society
Recently, cross-scene hyperspectral image classification(HSIC) via domain adaptation is drawing increasing attention. However, most existing methods either directly align the source domain and target domain without fully mining of SD information, or ...

Towards zero-shot human-object interaction detection via vision-language integration.

Neural networks : the official journal of the International Neural Network Society
Human-object interaction (HOI) detection aims to locate human-object pairs and identify their interaction categories in images. Most existing methods primarily focus on supervised learning, which relies on extensive manual HOI annotations. Such heavy...

Exploiting instance-label dynamics through reciprocal anchored contrastive learning for few-shot relation extraction.

Neural networks : the official journal of the International Neural Network Society
In the domain of Few-shot Relation Extraction (FSRE), the primary objective is to distill relational facts from limited labeled datasets. This task has recently witnessed significant advancements through the integration of Pre-trained Language Models...

Leveraging Context for Perceptual Prediction Using Word Embeddings.

Cognitive science
Word embeddings derived from large language corpora have been successfully used in cognitive science and artificial intelligence to represent linguistic meaning. However, there is continued debate as to how well they encode useful information about t...

ICPPNet: A semantic segmentation network model based on inter-class positional prior for scoliosis reconstruction in ultrasound images.

Journal of biomedical informatics
OBJECTIVE: Considering the radiation hazard of X-ray, safer, more convenient and cost-effective ultrasound methods are gradually becoming new diagnostic approaches for scoliosis. For ultrasound images of spine regions, it is challenging to accurately...

Linking Symptom Inventories Using Semantic Textual Similarity.

Journal of neurotrauma
An extensive library of symptom inventories has been developed over time to measure clinical symptoms of traumatic brain injury (TBI), but this variety has led to several long-standing issues. Most notably, results drawn from different settings and s...

Advancing hierarchical neural networks with scale-aware pyramidal feature learning for medical image dense prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Hierarchical neural networks are pivotal in medical imaging for multi-scale representation, aiding in tasks such as object detection and segmentation. However, their effectiveness is often limited by the loss of intra-scale ...

Utilizing semantically enhanced self-supervised graph convolution and multi-head attention fusion for herb recommendation.

Artificial intelligence in medicine
Traditional Chinese herbal medicine has long been recognized as an effective natural therapy. Recently, the development of recommendation systems for herbs has garnered widespread academic attention, as these systems significantly impact the applicat...

Information Geometric Approaches for Patient-Specific Test-Time Adaptation of Deep Learning Models for Semantic Segmentation.

IEEE transactions on medical imaging
The test-time adaptation (TTA) of deep-learning-based semantic segmentation models, specific to individual patient data, was addressed in this study. The existing TTA methods in medical imaging are often unconstrained, require anatomical prior inform...