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 101–111 of 111 papers

TitleStatusHype
PaLI: A Jointly-Scaled Multilingual Language-Image ModelCode0
Open-vocabulary vs. Closed-set: Best Practice for Few-shot Object Detection Considering Text DescribabilityCode0
CLIPPO: Image-and-Language Understanding from Pixels OnlyCode0
What Do You See? Enhancing Zero-Shot Image Classification with Multimodal Large Language ModelsCode0
Multilingual Vision-Language Pre-training for the Remote Sensing DomainCode0
Segment Any ChangeCode0
Altogether: Image Captioning via Re-aligning Alt-textCode0
Who's in and who's out? A case study of multimodal CLIP-filtering in DataCompCode0
MoDE: CLIP Data Experts via ClusteringCode0
Learning from Children: Improving Image-Caption Pretraining via CurriculumCode0
KPL: Training-Free Medical Knowledge Mining of Vision-Language ModelsCode0
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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