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Multi-Label Zero-Shot Learning Via Contrastive Label-Based Attention.
Int J Neural Syst
January 2025
Alibaba Cloud, Hangzhou, P. R. China.
Multi-label zero-shot learning (ML-ZSL) strives to recognize all objects in an image, regardless of whether they are present in the training data. Recent methods incorporate an attention mechanism to locate labels in the image and generate class-specific semantic information. However, the attention mechanism built on visual features treats label embeddings equally in the prediction score, leading to severe semantic ambiguity.
View Article and Find Full Text PDFMental preparation of karateka for sports competition in kata.
Front Sports Act Living
January 2025
Institute of Sport Science and Innovations, Lithuanian Sports University, Kaunas, Lithuania.
Mental preparation for sports competition in karate is significant, as it is deeply embedded in the philosophical and ethical values that underpin this combat method. In practice, the mental preparation of karateka varies depending on the type of competition, for example preparation for kata (forms) and kumite (fights). Thus, this perspective offers a concise account of the authors' viewpoint on the leading mental skills required of kata competitors.
View Article and Find Full Text PDFColor fundus photograph-based diabetic retinopathy grading via label relaxed collaborative learning on deep features and radiomics features.
Front Cell Dev Biol
January 2025
Department of Medical Informatics, Nantong University, Nantong, Jiangsu, China.
Introduction: Diabetic retinopathy (DR) has long been recognized as a common complication of diabetes, making accurate automated grading of its severity essential. Color fundus photographs play a crucial role in the grading of DR. With the advancement of artificial intelligence technologies, numerous researchers have conducted studies on DR grading based on deep features and radiomic features extracted from color fundus photographs.
View Article and Find Full Text PDFThe quagga mussel, : a novel model for EcoEvoDevo, environmental research, and the applied sciences.
Front Cell Dev Biol
January 2025
Department of Evolutionary Biology, Unit for Integrative Zoology, University of Vienna, Vienna, Austria.
Bivalve mollusks are globally distributed in marine and freshwater habitats. While exhibiting a relatively uniform bodyplan that is characterized by their eponymous bivalved shell that houses the soft-bodied animal, many lineages have acquired unique morphological, physiological, and molecular innovations that account for their high adaptability to the various properties of aquatic environments such as salinity, flow conditions, or substrate composition. This renders them ideal candidates for studies into the evolutionary trajectories that have resulted in their diversity, but also makes them important players for research concerned with climate change-induced warming and acidification of aquatic habitats.
View Article and Find Full Text PDFMambaTab: A Plug-and-Play Model for Learning Tabular Data.
Proc (IEEE Conf Multimed Inf Process Retr)
August 2024
Department of Computer Science, University of Kentucky, Lexington, KY, USA.
Despite the prevalence of images and texts in machine learning, tabular data remains widely used across various domains. Existing deep learning models, such as convolutional neural networks and transformers, perform well however demand extensive preprocessing and tuning limiting accessibility and scalability. This work introduces an innovative approach based on a structured state-space model (SSM), MambaTab, for tabular data.
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