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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 91–100 of 111 papers

TitleStatusHype
When are Lemons Purple? The Concept Association Bias of Vision-Language Models—0
CLIPPO: Image-and-Language Understanding from Pixels Only—0
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification—0
Generative Negative Text Replay for Continual Vision-Language Pretraining—0
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
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 Model—0
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—0
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training BenchmarkCode0
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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