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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 51–100 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
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification—0
Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models—0
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—0
RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training—0
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation—0
Retrieval-enriched zero-shot image classification in low-resource domains—0
Semantic Compositions Enhance Vision-Language Contrastive Learning—0
Soundify: Matching Sound Effects to Video—0
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives—0
Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities—0
Visual-Semantic Embedding Model Informed by Structured Knowledge—0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models—0
Zero-sample surface defect detection and classification based on semantic feedback neural network—0
Zero-Shot Image Classification Using Coupled Dictionary Embedding—0
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training BenchmarkCode0
Text-to-Image Diffusion Models are Zero-Shot ClassifiersCode0
DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language ModelsCode0
Unconstrained Open Vocabulary Image Classification: Zero-Shot Transfer from Text to Image via CLIP InversionCode0
Language-Driven Anchors for Zero-Shot Adversarial RobustnessCode0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
Do Vision-Language Foundational models show Robust Visual Perception?Code0
Image-Caption Encoding for Improving Zero-Shot GeneralizationCode0
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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