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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 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.
View Article and Find Full Text PDFDeep learning-based drug screening for the discovery of potential therapeutic agents for Alzheimer's disease.
J Pharm Anal
October 2024
College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
Alzheimer's disease (AD) is gradually increasing in prevalence and the complexity of its pathogenesis has led to a lengthy process of developing therapeutic drugs with limited success. Faced with this challenge, we proposed using a state-of-the-art drug screening algorithm to identify potential therapeutic compounds for AD from traditional Chinese medicine formulas with strong empirical support. We developed four deep neural network (DNN) models for AD drugs screening at the disease and target levels.
View Article and Find Full Text PDFIterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery.
Front Plant Sci
January 2025
Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, China.
Precise segmentation of unmanned aerial vehicle (UAV)-captured images plays a vital role in tasks such as crop yield estimation and plant health assessment in banana plantations. By identifying and classifying planted areas, crop areas can be calculated, which is indispensable for accurate yield predictions. However, segmenting banana plantation scenes requires a substantial amount of annotated data, and manual labeling of these images is both timeconsuming and labor-intensive, limiting the development of large-scale datasets.
View Article and Find Full Text PDFLead-Grouped Multi-Stage Learning for Myocardial Infarction Localization.
Methods
January 2025
School of Design, Hunan University, Changsha, 410082, China. Electronic address:
The electrocardiogram (ECG) is a ubiquitous medical diagnostic tool employed to localize myocardial infarction (MI) that is characterized by abnormal waveform patterns on the ECG. MI is a serious cardiovascular disease, and accurate, timely diagnosis is crucial for preventing severe outcomes. Current ECG analysis methods mainly rely on intra- and inter-lead feature extraction, but most models overlook the medical knowledge relevant to disease diagnosis.
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