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 51–75 of 111 papers

TitleStatusHype
KPL: Training-Free Medical Knowledge Mining of Vision-Language ModelsCode0
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation—0
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance—0
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives—0
Retrieval-enriched zero-shot image classification in low-resource domains—0
Multilingual Vision-Language Pre-training for the Remote Sensing DomainCode0
Altogether: Image Captioning via Re-aligning Alt-textCode0
Open-vocabulary vs. Closed-set: Best Practice for Few-shot Object Detection Considering Text DescribabilityCode0
CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language ModelsCode0
Do Vision-Language Foundational models show Robust Visual Perception?Code0
CoAPT: Context Attribute words for Prompt Tuning—0
Unconstrained Open Vocabulary Image Classification: Zero-Shot Transfer from Text to Image via CLIP InversionCode0
Semantic Compositions Enhance Vision-Language Contrastive Learning—0
BaFTA: Backprop-Free Test-Time Adaptation For Zero-Shot Vision-Language Models—0
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
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—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