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 26–50 of 111 papers

TitleStatusHype
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language RepresentationsCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Structure Pretraining and Prompt Tuning for Knowledge Graph TransferCode1
CHiLS: Zero-Shot Image Classification with Hierarchical Label SetsCode1
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse RetrievalCode1
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
LiT: Zero-Shot Transfer with Locked-image text TuningCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
Benchmarking Knowledge-driven Zero-shot LearningCode1
Open-vocabulary Object Detection via Vision and Language Knowledge DistillationCode1
Generative Multi-Label Zero-Shot LearningCode1
CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization—0
Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation—0
Bayesian Test-Time Adaptation for Vision-Language Models—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations—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