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Showing 451–475 of 1626 papers

TitleStatusHype
Deep Supervised Hashing with Triplet Labels—0
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection—0
Few-shot time-series anomaly detection with unsupervised domain adaptation—0
FILM: A Fast, Interpretable, and Low-rank Metric Learning Approach for Sentence Matching—0
DeepStory: Video Story QA by Deep Embedded Memory Networks—0
Dual-Triplet Metric Learning for Unsupervised Domain Adaptation in Video-Based Face Recognition—0
DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19—0
Analysis of Triplet Motifs in Biological Signed Oriented Graphs Suggests a Relationship Between Fine Topology and Function—0
Dynamical Characteristics of Global Stock Markets Based on Time Dependent Tsallis Non-Extensive Statistics and Generalized Hurst Exponents—0
Dynamic Entity Memory Network for Dialogue Relational Triplet Extraction—0
3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances—0
An Online Algorithm for Learning Selectivity to Mixture Means—0
Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection—0
Edgeworth Expansion for Semi-hard Triplet Loss—0
Cheap-fake Detection with LLM using Prompt Engineering—0
Adversarial Binary Coding for Efficient Person Re-identification—0
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss—0
Few-shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition—0
Deep Similarity Learning Loss Functions in Data Transformation for Class Imbalance—0
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss—0
Deep Similarity Learning for Sports Team Ranking—0
Deep Set Neural Networks for forecasting asynchronous bioprocess timeseries—0
Deep Semantic Hashing with Generative Adversarial Networks—0
EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling—0
An Adversarial Approach to Hard Triplet Generation—0
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