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 1–10 of 111 papers

TitleStatusHype
CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization—0
LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer TextCode1
Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation—0
Bayesian Test-Time Adaptation for Vision-Language Models—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations—0
Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality InversionCode2
KPL: Training-Free Medical Knowledge Mining of Vision-Language ModelsCode0
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation—0
Post-hoc Probabilistic Vision-Language ModelsCode1
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All datasetsCountry211ICinWODinW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CLIP (ViT B-32)Average Score56.64—Unverified