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New synthetic dataset created for multimodal learning competition

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a pipeline to create a synthetic dataset of image-based instruction-response pairs for the ILSP DCVLR Competition. This process utilizes the Cauldron collection and the Qwen2.5-VL-7B Instruct model for generating instructions, which are then filtered using a DeBERTa-v3 reward model. The resulting dataset, containing instructions across math, spatial, and social reasoning domains, shows a median reward score of approximately 1.53, indicating high quality. AI

IMPACT This synthetic dataset could advance multimodal learning by providing diverse image-conditioned instruction pairs for AI models.

RANK_REASON The cluster describes a research paper submitted to a competition, detailing the creation of a synthetic dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New synthetic dataset created for multimodal learning competition

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14 / 100
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The cluster describes a research paper submitted to a competition, detailing the creation of a synthetic dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Eleni E. Kasoura, Dimitrios Damianos, Georgios Paraskevopoulos, Vassilios Katsouros ·

    Instruction Mining From Images: Constructing a Synthetic Dataset for Multimodal Learning ILSP DCVLR Competition Submission

    arXiv:2610.19932v1 Announce Type: new Abstract: This work presents a compact and reproducible pipeline for synthesizing and curating image-based instruction-response pairs for DCVLR. We draw from the Cauldron collection and use Magpie-style instruction and response synthesis with…

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