Comprehension of Multilingual Expressions Referring to Target Objects in Visual Inputs

• URL: http://arxiv.org/abs/2511.11427v1
• Date: Fri, 14 Nov 2025 15:54:34 GMT
• Title: Comprehension of Multilingual Expressions Referring to Target Objects in Visual Inputs
• Abstract summary: Referring Expression (REC) requires models to localize objects in images based on natural language descriptions.<n>This work addresses multilingual REC through two main contributions.<n>We construct a unified multilingual dataset spanning 10 languages, by systematically expanding 12 existing English REC benchmarks through machine translation and context-based translation enhancement.<n>The resulting dataset comprises approximately 8 million multilingual referring expressions across 177,620 images, with 336,882 annotated objects.
• Score: 47.944645462877894
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Referring Expression Comprehension (REC) requires models to localize objects in images based on natural language descriptions. Research on the area remains predominantly English-centric, despite increasing global deployment demands. This work addresses multilingual REC through two main contributions. First, we construct a unified multilingual dataset spanning 10 languages, by systematically expanding 12 existing English REC benchmarks through machine translation and context-based translation enhancement. The resulting dataset comprises approximately 8 million multilingual referring expressions across 177,620 images, with 336,882 annotated objects. Second, we introduce an attention-anchored neural architecture that uses multilingual SigLIP2 encoders. Our attention-based approach generates coarse spatial anchors from attention distributions, which are subsequently refined through learned residuals. Experimental evaluation demonstrates competitive performance on standard benchmarks, e.g. achieving 86.9% accuracy at IoU@50 on RefCOCO aggregate multilingual evaluation, compared to an English-only result of 91.3%. Multilingual evaluation shows consistent capabilities across languages, establishing the practical feasibility of multilingual visual grounding systems. The dataset and model are available at $\href{https://multilingual.franreno.com}{multilingual.franreno.com}$.

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