Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations (2024.acl-long)
Copied to clipboard
| Challenge: | Vision-and-language models with separate encoders for each modality are limited in availability. |
| Approach: | They propose a multilingual benchmark that offers (partial) translations of ImageNet labels to 100 languages, built without machine translation or manual annotation. |
| Outcome: | The proposed model outperforms models on English and low-resource languages. |
Similar Papers
MVL-SIB: A Massively Multilingual Vision-Language Benchmark for Cross-Modal Topical Matching (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing multilingual vision-language (VL) benchmarks typically only cover a handful of languages, underscoring the need for evaluation data for low-resource languages. |
| Approach: | They propose a multilingual vision-language benchmark that evaluates cross-modal and text-only topical matching across 205 languages. |
| Outcome: | The proposed model performs better in cross-modal and text-only topical matching in lower-resource languages than the most multilingual benchmarks. |
Visually Grounded Reasoning across Languages and Cultures (2021.emnlp-main)
Copied to clipboard
| Challenge: | a new protocol allows for a multilingual hierarchy of concepts and images based on native speakers . the results suggest that the current models are not robust enough to handle multilingual data . |
| Approach: | They propose a protocol to construct an ImageNet-style hierarchy representative of more languages and cultures. |
| Outcome: | The proposed protocol lets the selection of concepts and images be entirely driven by native speakers, rather than scraping them automatically. |
Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset (2022.lrec-1)
Copied to clipboard
| Challenge: | The goal of the project Multilingual Image Corpus is to provide a large image dataset with annotated objects and object descriptions in 24 languages. |
| Approach: | They propose to provide a large image dataset with annotated objects and object descriptions in 24 languages. |
| Outcome: | The project provides a large image dataset with annotated objects and object descriptions in 24 languages. |
Evaluation of Multilingual Image Captioning: How far can we get with CLIP models? (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to evaluate image captions are English-centric, despite improvements in the CLIPScore metric . however, there are no available benchmarks for multilingual captioning evaluation . |
| Approach: | They propose to use machine-translated and machine-repurposed datasets to evaluate CLIPScore variants in multilingual settings. |
| Outcome: | The proposed evaluation strategies are based on machine-translated and human judgements. |
Learning Translations via Images with a Massively Multilingual Image Dataset (P18-1)
Copied to clipboard
| Challenge: | Existing datasets for learning translations of words are limited to a few high-resource languages and unrealistically easy settings. |
| Approach: | They propose a large-scale multilingual corpus of images labeled with the word they represent to facilitate translation research. |
| Outcome: | The proposed method improves on an unsupervised technique that has been limited to a few languages and unrealistic settings. |
Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models (2021.naacl-main)
Copied to clipboard
| Challenge: | a new study examines zero-shot cross-lingual transfer of vision-language models . we study multilingual text-to-video search in non-English languages without annotations . |
| Approach: | They propose a Transformer-based model that learns contextual multilingual multimodal embeddings . they propose 'zero-shot cross-lingual transfer' to improve multilingual search . |
| Outcome: | The proposed model outperforms baselines on multilingual text-to-video search and multilingual image search on VTT and VATEX. |
VLURes: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | ***VLURes** provides a practical testbed for long-text grounding and multilingual robustness in web-realistic agent settings. |
| Approach: | They propose a multilingual benchmark for evaluating vision-language models under long-text grounding. |
| Outcome: | ***VLURes** provides a testbed for long-text grounding and multilingual robustness in web-realistic agent settings. |
Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks (2023.findings-acl)
Copied to clipboard
| Challenge: | Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. |
| Approach: | They propose to use open-source, open-access language models to make visual input accessible to the model using separate verbalisation models. |
| Outcome: | The proposed model can handle visual input but also require strong reasoning component. |
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to improve multilingual neural machine translation (NMT) are weak, and lack robustness to support language pairs with varying typological characteristics. |
| Approach: | They propose to deepen NMT models to support language pairs with varying typological characteristics by random online backtranslation. |
| Outcome: | The proposed approach narrows the performance gap with bilingual models and improves zero-shot performance by 10 BLEU, approaching conventional pivot-based methods. |
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are becoming general-purpose APIs, requiring visual knowledge to be understood. |
| Approach: | They propose to evaluate the visual capability of large-scale large-language models through visual commonsense evaluation using a human-annotated dataset. |
| Outcome: | The proposed dataset compares the visual commonsense knowledge of large-scale models with those of unimodal LLMs and visually augmented models. |