mlsanityData to 7 Oct 2026

VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks

arXiv 2407.19795 ↗First posted 29 Jul 2024cs.CL cs.AI cs.CVcited by 3

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Abstract (arXiv)

Domain generalizability is a crucial aspect of a deep learning model since it determines the capability of the model to perform well on data from unseen domains. However, research on the domain generalizability of deep learning models for vision-language tasks remains limited, primarily because of the lack of required datasets. To address these challenges, we propose VolDoGer: Vision-Language Dataset for Domain Generalization, a dedicated dataset designed for domain generalization that addresses three vision-language tasks: image captioning, visual question answering, and visual entailment. We constructed VolDoGer by extending LLM-based data annotation techniques to vision-language tasks, thereby alleviating the burden of recruiting human annotators. We evaluated the domain generalizability of various models, ranging from fine-tuned models to a recent multimodal large language model, through VolDoGer.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 57 of its 91 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

57 of 91 references.

References · 57

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50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoningcited by 10
  2. 2026Reasoning-Driven Multimodal LLM for Domain Generalizationcited by 7
  3. 2025Seeing, Signing, and Saying: A Vision-Language Model-Assisted Pipeline for Sign Language Data Acquisition and Curation from Social Mediacited by 0

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