SOTAVerified

Zero-Shot Image Classification

Zero-shot image classification is a technique in computer vision where a model can classify images into categories that were not present during training. This is achieved by leveraging semantic information about the categories, such as textual descriptions or relationships between classes.

Papers

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Showing 76–100 of 111 papers

TitleStatusHype
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification—0
Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models—0
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—0
RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training—0
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation—0
Retrieval-enriched zero-shot image classification in low-resource domains—0
Semantic Compositions Enhance Vision-Language Contrastive Learning—0
Soundify: Matching Sound Effects to Video—0
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives—0
Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities—0
Visual-Semantic Embedding Model Informed by Structured Knowledge—0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models—0
Zero-sample surface defect detection and classification based on semantic feedback neural network—0
Zero-Shot Image Classification Using Coupled Dictionary Embedding—0
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training BenchmarkCode0
Text-to-Image Diffusion Models are Zero-Shot ClassifiersCode0
DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language ModelsCode0
Unconstrained Open Vocabulary Image Classification: Zero-Shot Transfer from Text to Image via CLIP InversionCode0
Language-Driven Anchors for Zero-Shot Adversarial RobustnessCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Do Vision-Language Foundational models show Robust Visual Perception?Code0
Image-Caption Encoding for Improving Zero-Shot GeneralizationCode0
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All datasetsCountry211ICinWODinW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OpenClip H/14 (34B)(Laion2B)Top-1 accuracy30.01—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP (ViT B-32)Average Score56.64—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIP (Tiny A)Average Score11.4—Unverified