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 51–75 of 111 papers

TitleStatusHype
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense PredictionCode2
Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models—0
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training—0
PerceptionCLIP: Visual Classification by Inferring and Conditioning on ContextsCode1
PromptStyler: Prompt-driven Style Generation for Source-free Domain GeneralizationCode1
Distilling Large Vision-Language Model with Out-of-Distribution GeneralizabilityCode1
RemoteCLIP: A Vision Language Foundation Model for Remote SensingCode2
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language RepresentationsCode1
Semantically-Prompted Language Models Improve Visual Descriptions—0
Learning from Children: Improving Image-Caption Pretraining via CurriculumCode0
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Text-to-Image Diffusion Models are Zero-Shot ClassifiersCode0
Structure Pretraining and Prompt Tuning for Knowledge Graph TransferCode1
CHiLS: Zero-Shot Image Classification with Hierarchical Label SetsCode1
Language-Driven Anchors for Zero-Shot Adversarial RobustnessCode0
Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities—0
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse RetrievalCode1
RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models—0
CLIPPO: Image-and-Language Understanding from Pixels OnlyCode0
Reproducible scaling laws for contrastive language-image learningCode1
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification—0
AltCLIP: Altering the Language Encoder in CLIP for Extended Language CapabilitiesCode4
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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