Omni-NegCLIP improves CLIP's negation understanding by up to 52.65% on presence-based and 12.50% on absence-based tasks through front-layer fine-tuning with specialized contrastive losses.
Learning transferable visual models from natural language supervi- sion
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
verdicts
UNVERDICTED 5representative citing papers
Natural-domain foundation models provide competitive and more robust priors than task-specific models for accelerated cardiac MRI reconstruction in cross-domain settings.
RADSeg adapts the RADIO model with targeted enhancements to deliver 6-30% higher mIoU in zero-shot OVSS while using 2.5x fewer parameters and running 3.95x faster than prior large-model combinations.
DetRefiner fuses global and local features with a Transformer to refine OVOD confidence scores, delivering up to +10.1 AP gains on novel categories across multiple datasets.
MV3DIS uses 3D-guided mask matching and depth consistency to produce more consistent multi-view 2D masks that refine into accurate zero-shot 3D instances.
citing papers explorer
-
Omni-NegCLIP: Enhancing CLIP with Front-Layer Contrastive Fine-Tuning for Comprehensive Negation Understanding
Omni-NegCLIP improves CLIP's negation understanding by up to 52.65% on presence-based and 12.50% on absence-based tasks through front-layer fine-tuning with specialized contrastive losses.
-
Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?
Natural-domain foundation models provide competitive and more robust priors than task-specific models for accelerated cardiac MRI reconstruction in cross-domain settings.
-
RADSeg: Unleashing Parameter and Compute Efficient Zero-Shot Open-Vocabulary Segmentation Using Agglomerative Models
RADSeg adapts the RADIO model with targeted enhancements to deliver 6-30% higher mIoU in zero-shot OVSS while using 2.5x fewer parameters and running 3.95x faster than prior large-model combinations.
-
DetRefiner: Model-Agnostic Detection Refinement with Feature Fusion Transformer
DetRefiner fuses global and local features with a Transformer to refine OVOD confidence scores, delivering up to +10.1 AP gains on novel categories across multiple datasets.
-
MV3DIS: Multi-View Mask Matching via 3D Guides for Zero-Shot 3D Instance Segmentation
MV3DIS uses 3D-guided mask matching and depth consistency to produce more consistent multi-view 2D masks that refine into accurate zero-shot 3D instances.