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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 76–100 of 111 papers

TitleStatusHype
Chinese CLIP: Contrastive Vision-Language Pretraining in ChineseCode5
Generative Negative Text Replay for Continual Vision-Language Pretraining—0
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
General Image Descriptors for Open World Image Retrieval using ViT CLIPCode1
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss—0
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
PaLI: A Jointly-Scaled Multilingual Language-Image ModelCode0
What does a platypus look like? Generating customized prompts for zero-shot image classificationCode2
Zero-Shot Temporal Action Detection via Vision-Language PromptingCode1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
Disentangled Ontology Embedding for Zero-shot LearningCode1
Masked Unsupervised Self-training for Label-free Image ClassificationCode1
CCMB: A Large-scale Chinese Cross-modal BenchmarkCode1
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—0
Zero-Shot Logit AdjustmentCode1
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual ModelsCode4
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image ClassificationCode1
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training BenchmarkCode0
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision—0
Soundify: Matching Sound Effects to Video—0
LiT: Zero-Shot Transfer with Locked-image text TuningCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
Benchmarking Knowledge-driven Zero-shot LearningCode1
Zero-sample surface defect detection and classification based on semantic feedback neural network—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