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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 91–100 of 111 papers

TitleStatusHype
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual ModelsCode4
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image ClassificationCode1
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training BenchmarkCode0
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision—0
Soundify: Matching Sound Effects to Video—0
LiT: Zero-Shot Transfer with Locked-image text TuningCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
Benchmarking Knowledge-driven Zero-shot LearningCode1
Zero-sample surface defect detection and classification based on semantic feedback neural network—0
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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