ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models

Autor: Kesen, Ilker, Pedrotti, Andrea, Dogan, Mustafa, Cafagna, Michele, Acikgoz, Emre Can, Parcalabescu, Letitia, Calixto, Iacer, Frank, Anette, Gatt, Albert, Erdem, Aykut, Erdem, Erkut

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present Vi

Externí odkaz: http://arxiv.org/abs/2311.07022

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Heterogeneous document embeddings for cross-lingual text classification

Autor: Moreo, Alejandro, Pedrotti, Andrea, Sebastiani, Fabrizio

Publikováno v: SAC
SAC 2021: 36th ACM/SIGAPP Symposium On Applied Computing, pp. 685–688, Online conference, 22-26/03/2021
info:cnr-pdr/source/autori:Moreo A.; Pedrotti A.; Sebastiani F./congresso_nome:SAC 2021: 36th ACM%2FSIGAPP Symposium On Applied Computing/congresso_luogo:Online conference/congresso_data:22-26%2F03%2F2021/anno:2021/pagina_da:685/pagina_a:688/intervallo_pagine:685–688
Proceedings of the 36th Annual ACM Symposium on Applied Computing
SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

Funnelling (Fun) is a method for cross-lingual text classification (CLC) based on a two-tier ensemble for heterogeneous transfer learning. In Fun, 1st-tier classifiers, each working on a different, language-dependent feature space, return a vector of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6dbede8e63d37e5f652c426a613197da
https://doi.org/10.1145/3412841.3442093