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
Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive LearningCode1
Open-vocabulary Object Detection via Vision and Language Knowledge DistillationCode1
Distilling Large Vision-Language Model with Out-of-Distribution GeneralizabilityCode1
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse RetrievalCode1
Reproducible scaling laws for contrastive language-image learningCode1
Benchmarking Knowledge-driven Zero-shot LearningCode1
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive LearningCode1
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual KnowledgeCode1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image ClassificationCode1
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language RepresentationsCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
Learn "No" to Say "Yes" Better: Improving Vision-Language Models via NegationsCode1
Can We Talk Models Into Seeing the World Differently?Code1
Generative Multi-Label Zero-Shot LearningCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer TextCode1
LiT: Zero-Shot Transfer with Locked-image text TuningCode1
Structure Pretraining and Prompt Tuning for Knowledge Graph TransferCode1
Zero-Shot Logit AdjustmentCode1
DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language ModelsCode0
Do Vision-Language Foundational models show Robust Visual Perception?Code0
PaLI: A Jointly-Scaled Multilingual Language-Image ModelCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Language-Driven Anchors for Zero-Shot Adversarial RobustnessCode0
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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