Pith

open record

sign in

arxiv: 2108.08688 · v1 · pith:KLTCSYDM · submitted 2021-08-19 · cs.CL · cs.CV

Contrastive Language-Image Pre-training for the Italian Language

Federico Bianchi , Giuseppe Attanasio , Raphael Pisoni , Silvia Terragni , Gabriele Sarti , Sri Lakshmi This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 reserved pith:KLTCSYDMrecord.jsonopen to challenge →

classification cs.CL cs.CV
keywords modelcliplanguageclassificationclip-italiancontrastivedataitalian
0
0 comments X
read the original abstract

CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts. The model is trained on a massive amount of English data and shows impressive performance on zero-shot classification tasks. Training the same model on a different language is not trivial, since data in other languages might be not enough and the model needs high-quality translations of the texts to guarantee a good performance. In this paper, we present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs. Results show that CLIP-Italian outperforms the multilingual CLIP model on the tasks of image retrieval and zero-shot classification.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

    cs.CV 2023-12 unverdicted novelty 5.0

    InternVL scales a vision model to 6B parameters and aligns it with LLMs using web data to achieve state-of-the-art results on 32 visual-linguistic benchmarks.