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Semantics

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Using drawings and deep neural networks to characterize the building blocks of human visual similarity.

Memory & cognition
Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neur...

On knowing a gene: A distributional hypothesis of gene function.

Cell systems
As words can have multiple meanings that depend on sentence context, genes can have various functions that depend on the surrounding biological system. This pleiotropic nature of gene function is limited by ontologies, which annotate gene functions w...

Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping.

Sensors (Basel, Switzerland)
Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolu...

Multi-grained visual pivot-guided multi-modal neural machine translation with text-aware cross-modal contrastive disentangling.

Neural networks : the official journal of the International Neural Network Society
The goal of multi-modal neural machine translation (MNMT) is to incorporate language-agnostic visual information into text to enhance the performance of machine translation. However, due to the inherent differences between image and text, these two m...

Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks.

PLoS computational biology
Responses to natural stimuli in area V4-a mid-level area of the visual ventral stream-are well predicted by features from convolutional neural networks (CNNs) trained on image classification. This result has been taken as evidence for the functional ...

Enhancing the coverage of SemRep using a relation classification approach.

Journal of biomedical informatics
OBJECTIVE: Relation extraction is an essential task in the field of biomedical literature mining and offers significant benefits for various downstream applications, including database curation, drug repurposing, and literature-based discovery. The b...

Unsupervised domain adaptive segmentation algorithm based on two-level category alignment.

Neural networks : the official journal of the International Neural Network Society
To enhance the model's generalization ability in unsupervised domain adaptive segmentation tasks, most approaches have primarily focused on pixel-level local features, but neglected the clue in category information. This limitation results in the seg...

Structure enhanced prototypical alignment for unsupervised cross-domain node classification.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have demonstrated remarkable success in graph node classification task. However, their performance heavily relies on the availability of high-quality labeled data, which can be time-consuming and labor-intensive to acquir...

Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue.

Computers in biology and medicine
BACKGROUND: Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological s...

Computational reconstruction of mental representations using human behavior.

Nature communications
Revealing how the mind represents information is a longstanding goal of cognitive science. However, there is currently no framework for reconstructing the broad range of mental representations that humans possess. Here, we ask participants to indicat...