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
Bayesian Test-Time Adaptation for Vision-Language Models—0
Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation—0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification—0
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance—0
CoAPT: Context Attribute words for Prompt Tuning—0
CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Efficient Model-Agnostic Multi-Group Equivariant Networks—0
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss—0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—0
Gaze Embeddings for Zero-Shot Image Classification—0
Generative Negative Text Replay for Continual Vision-Language Pretraining—0
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training—0
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification—0
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification—0
Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification—0
It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap—0
Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions—0
Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions—0
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships—0
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models—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