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Showing 676–700 of 1626 papers

TitleStatusHype
Active Metric Learning from Relative Comparisons—0
Cluster Loss for Person Re-Identification—0
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss—0
Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Loss—0
Appearance Learning for Image-based Motion Estimation in Tomography—0
Enhanced Data Transfer Cooperating with Artificial Triplets for Scene Graph Generation—0
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not—0
A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction—0
Energy Score-based Pseudo-Label Filtering and Adaptive Loss for Imbalanced Semi-supervised SAR target recognition—0
CLERF: Contrastive LEaRning for Full Range Head Pose Estimation—0
End-to-End triplet loss based fine-tuning for network embedding in effective PII detection—0
Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification—0
End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms—0
Class Interference Regularization—0
End-to-End Illuminant Estimation Based on Deep Metric Learning—0
End-to-End Differentiable 6DoF Object Pose Estimation with Local and Global Constraints—0
Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)—0
Any-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining—0
AnyFace: Free-style Text-to-Face Synthesis and Manipulation—0
Active metric learning and classification using similarity queries—0
Empirical Risk Minimization under Random Censorship: Theory and Practice—0
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues—0
Classification of freshwater snails of the genus Radomaniola with multimodal triplet networks—0
EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling—0
Embedding Label Structures for Fine-Grained Feature Representation—0
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