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 1–50 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
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
TaxaBind: A Unified Embedding Space for Ecological ApplicationsCode1
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
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual KnowledgeCode1
CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet UpcyclingCode2
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
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