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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 1–25 of 111 papers

TitleStatusHype
Chinese CLIP: Contrastive Vision-Language Pretraining in ChineseCode5
AltCLIP: Altering the Language Encoder in CLIP for Extended Language CapabilitiesCode4
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual ModelsCode4
PromptKD: Unsupervised Prompt Distillation for Vision-Language ModelsCode3
CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet UpcyclingCode2
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent CollaborationCode2
What does a platypus look like? Generating customized prompts for zero-shot image classificationCode2
RemoteCLIP: A Vision Language Foundation Model for Remote SensingCode2
WATT: Weight Average Test-Time Adaptation of CLIPCode2
Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality InversionCode2
Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIPCode2
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text SupervisionCode2
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense PredictionCode2
CHiLS: Zero-Shot Image Classification with Hierarchical Label SetsCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Generative Multi-Label Zero-Shot LearningCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
General Image Descriptors for Open World Image Retrieval using ViT CLIPCode1
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse RetrievalCode1
LiT: Zero-Shot Transfer with Locked-image text TuningCode1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual KnowledgeCode1
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
Benchmarking Knowledge-driven Zero-shot LearningCode1
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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