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ARXIV:2605.23482 · VISION-LANGUAGE DATASET DISTILLATION · SUBMITTED 25 MAY · 20:33 UTC · FRESHNESS STALE
ARXIV:2605.23482VISION-LANGUAGE DATASET DISTILLATIONSUBMITTED 25 MAY · 20:33 UTCFRESHNESS STALEJongoh Jeong · Hoyong Kwon · Minseok Kim · Kuk-Jin Yoon · arXiv
A geometry-aware framework for efficient multimodal dataset distillation that preserves cross-modal alignment and reduces computational cost.
Opportunity summary
Pain A geometry-aware framework for efficient multimodal dataset distillation that preserves cross-modal alignment and reduces computational cost.
Evidence 0 refs | 4 sources | 50% coverage
Blocker Evidence unverified
A geometry-aware framework for efficient multimodal dataset distillation that preserves cross-modal alignment and reduces computational cost. As modern systems increasingly operate on paired vision-language inputs, multimodal distillation must preserve representation quality and cross-modal alignment…
Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increasingly operate on paired vision-language inputs, multimodal distillation must preserve representation quality and cross-modal alignment under tight…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across image-text retrieval benchmarks with cross-architecture evaluation, MDM yields compact synthetic sets that preserve multimodal semantics, substantially reduce distillation cost, and remain robust across…
Vision-Language Dataset Distillation moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A geometry-aware framework for efficient multimodal dataset distillation that preserves cross-modal alignment and reduces computational cost.
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10.48550/arXiv.2605.23482A geometry-aware framework for efficient multimodal dataset distillation that preserves cross-modal alignment and reduces computational cost.
Abstract
Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increasingly operate on paired vision-language inputs, multimodal distillation must preserve representation quality and cross-modal alignment under tight compute and memory budgets, yet prior methods often require heavy computes and overlook their correlations. To address this, we present Multimodal Distribution Matching (MDM), a geometry-aware framework for efficient and generalizable multimodal distillation. Specifically, MDM integrates complementary components at the data, model, and loss levels. At the data level, it initializes synthetic image-text pairs by sampling from clusters in the joint embedding space. At the model level, it forms a mixed teacher by interpolating independently fine-tuned models in weight space according to their angular deviation from the pretrained anchor. At the loss level, it matches joint distributions on the unit hypersphere using a geometry-aware matching objective that exploits the joint features in the cross-modal agreement and discrepancy directions along with symmetric contrastive learning. Across image-text retrieval benchmarks with cross-architecture evaluation, MDM yields compact synthetic sets that preserve multimodal semantics, substantially reduce distillation cost, and remain robust across architectures.
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PROBLEM
A geometry-aware framework for efficient multimodal dataset distillation that preserves cross-modal alignment and reduces computational cost. As modern systems increasingly operate on paired vision-language inputs, multimodal distillation must preserve representation quality and...
METHOD
Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increasingly operate on paired vision-language inputs, multimodal distillation must preserve representation quality and cross-modal alig...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across image-text retrieval benchmarks with cross-architecture evaluation, MDM yields compact synthetic sets that preserve multimodal semantics, substantially reduce distillation cost, and remain robust a...
WHY NOW
Vision-Language Dataset Distillation moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A geometry-aware framework for efficient multimodal dataset distillation that preserves cross-modal alignment and reduces computational cost. As modern systems increasingly operate on paired vision-language inputs, multimodal distillation must preserve representation quality and cross-modal alignment under tight compute and memory budgets, yet prior methods often require heavy computes and overlook their correlations.
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partial
Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increasingly operate on paired vision-language inputs, multimodal distillation must preserve representation quality and cross-modal alignment under tight compute and memory budgets, yet prior methods often require heavy computes and overlook their correlations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across image-text retrieval benchmarks with cross-architecture evaluation, MDM yields compact synthetic sets that preserve multimodal semantics, substantially reduce distillation cost, and remain robust across architectures. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-Language Dataset Distillation moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A geometry-aware framework for efficient multimodal dataset distillation that preserves cross-modal alignment and reduces computational cost.
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Vision-Language Dataset Distillation
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