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zero-shot-classification

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TitleStatusHype
Pushing the Limits of Vision-Language Models in Remote Sensing without Human Annotations—0
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—0
RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability—0
RECLIP: Resource-efficient CLIP by Training with Small Images—0
Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation—0
Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning—0
Refining CLIP's Spatial Awareness: A Visual-Centric Perspective—0
Relation-based Generalized Zero-shot Classification with the Domain Discriminator on the shared representation—0
ResiDual Transformer Alignment with Spectral Decomposition—0
Robust Cross-Modal Representation Learning with Progressive Self-Distillation—0
S^3: Synonymous Semantic Space for Improving Zero-Shot Generalization of Vision-Language Models—0
Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection—0
Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models—0
Selective Zero-Shot Classification with Augmented Attributes—0
Semantic Borrowing for Generalized Zero-Shot Learning—0
Semantic Compositions Enhance Vision-Language Contrastive Learning—0
Semantic Graph for Zero-Shot Learning—0
Semantic Softmax Loss for Zero-Shot Learning—0
Semi-Supervised Zero-Shot Classification With Label Representation Learning—0
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features—0
Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders—0
SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentation—0
SILC: Improving Vision Language Pretraining with Self-Distillation—0
SLANT: Spurious Logo ANalysis Toolkit—0
Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification—0
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