AIMC Topic: Semantics

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IPGN: Interactiveness Proposal Graph Network for Human-Object Interaction Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Human-Object Interaction (HOI) Detection is an important task to understand how humans interact with objects. Most of the existing works treat this task as an exhaustive triplet 〈 human, verb, object 〉 classification problem. In this paper, we decomp...

Improved biomedical word embeddings in the transformer era.

Journal of biomedical informatics
BACKGROUND: Recent natural language processing (NLP) research is dominated by neural network methods that employ word embeddings as basic building blocks. Pre-training with neural methods that capture local and global distributional properties (e.g.,...

The Infectious Disease Ontology in the age of COVID-19.

Journal of biomedical semantics
BACKGROUND: Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially f...

The neural representation of abstract words may arise through grounding word meaning in language itself.

Human brain mapping
In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of b...

BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.

Journal of biomedical semantics
BACKGROUND: Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-relat...

Lidar-Camera Semi-Supervised Learning for Semantic Segmentation.

Sensors (Basel, Switzerland)
In this work, we investigated two issues: (1) How the fusion of lidar and camera data can improve semantic segmentation performance compared with the individual sensor modalities in a supervised learning context; and (2) How fusion can also be levera...

Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester.

Sensors (Basel, Switzerland)
Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to ag...

Semantic Textual Similarity in Japanese Clinical Domain Texts Using BERT.

Methods of information in medicine
BACKGROUND: Semantic textual similarity (STS) captures the degree of semantic similarity between texts. It plays an important role in many natural language processing applications such as text summarization, question answering, machine translation, i...

Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation.

Sensors (Basel, Switzerland)
With the increase in the digitization efforts of herbarium collections worldwide, dataset repositories such as iDigBio and GBIF now have hundreds of thousands of herbarium sheet images ready for exploration. Although this serves as a new source of pl...

Quadruplet-Based Deep Cross-Modal Hashing.

Computational intelligence and neuroscience
Recently, benefitting from the storage and retrieval efficiency of hashing and the powerful discriminative feature extraction capability of deep neural networks, deep cross-modal hashing retrieval has drawn more and more attention. To preserve the se...