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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 31–40 of 111 papers

TitleStatusHype
Reproducible scaling laws for contrastive language-image learningCode1
General Image Descriptors for Open World Image Retrieval using ViT CLIPCode1
Zero-Shot Temporal Action Detection via Vision-Language PromptingCode1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
Disentangled Ontology Embedding for Zero-shot LearningCode1
Masked Unsupervised Self-training for Label-free Image ClassificationCode1
CCMB: A Large-scale Chinese Cross-modal BenchmarkCode1
Zero-Shot Logit AdjustmentCode1
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image ClassificationCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
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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