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
Semantic Compositions Enhance Vision-Language Contrastive Learning—0
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent CollaborationCode2
Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIPCode2
WATT: Weight Average Test-Time Adaptation of CLIPCode2
BaFTA: Backprop-Free Test-Time Adaptation For Zero-Shot Vision-Language Models—0
Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive LearningCode1
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships—0
It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap—0
What Do You See? Enhancing Zero-Shot Image Classification with Multimodal Large Language ModelsCode0
Who's in and who's out? A case study of multimodal CLIP-filtering in DataCompCode0
Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification—0
MoDE: CLIP Data Experts via ClusteringCode0
A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene—0
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive LearningCode1
Learn "No" to Say "Yes" Better: Improving Vision-Language Models via NegationsCode1
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
Can We Talk Models Into Seeing the World Differently?Code1
PromptKD: Unsupervised Prompt Distillation for Vision-Language ModelsCode3
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—0
Image-Caption Encoding for Improving Zero-Shot GeneralizationCode0
Segment Any ChangeCode0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models—0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Efficient Model-Agnostic Multi-Group Equivariant Networks—0
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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