Vocabulary-free Image Classification
2 papers with code • 0 benchmarks • 0 datasets
Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categories, a.k.a. the vocabulary, is assumed at test time for composing the textual prompts. However, such assumption can be impractical when the semantic context is unknown and evolving. Vocabulary-free Image Classification (VIC) aims to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary.
Benchmarks
These leaderboards are used to track progress in Vocabulary-free Image Classification
Most implemented papers
Vocabulary-free Image Classification
We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary.
Vocabulary-free Image Classification and Semantic Segmentation
altndrr/vicss
•
•
To address VIC, we propose Category Search from External Databases (CaSED), a training-free method that leverages a pre-trained vision-language model and an external database.
