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SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?

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arxiv 2402.01832 v2 pith:QU542EDE submitted 2024-02-02 cs.CV cs.AIcs.LG

Hasan Abed Al Kader Hammoud , Hani Itani , Fabio Pizzati , Philip Torr , Adel Bibi , Bernard Ghanem This is my paper
classification cs.CVcs.AIcs.LG
keywords syntheticclipdatamodelssynthcliptrainedimagesanalysis
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We present SynthCLIP, a CLIP model trained on entirely synthetic text-image pairs. Leveraging recent text-to-image (TTI) networks and large language models (LLM), we generate synthetic datasets of images and corresponding captions at scale, with no human intervention. In this work, we provide an analysis on CLIP models trained on synthetic data. We provide insights on the data generation strategy, number of samples required, scaling trends, and resulting properties. We also introduce SynthCI-30M, a purely synthetic dataset comprising 30 million captioned images. Our code, trained models, and data, are released as open source at https://github.com/hammoudhasan/SynthCLIP

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting

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    Post-generation selection via Homogeneous-Heterogeneous real-data splits and a fidelity-diversity score raises synthetic-image utility for classification and segmentation without retraining generators.

  3. Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis

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    Poplar-9K is a curated human-centric image dataset generated through a reproducible attribute sampling, diffusion rendering, and vision-language inspection pipeline, with 9,401 accepted pairs from 11,765 candidates.

  4. LoRA-Loop: Closing the Synthetic Replay Cycle for Continual VLM Learning

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Adapting a text-to-image generator with task-specific LoRA adapters and filtering samples by the model's own confidence improves synthetic replay in continual vision-language learning.

  5. FIX-CLIP: Dual-Branch Hierarchical Contrastive Learning via Synthetic Captions for Better Understanding of Long Text

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    A dual-branch CLIP training pipeline with regional prompts and hierarchical feature alignment reaches state-of-the-art on long- and short-text retrieval.

  6. MobileCLIP2: Improving Multi-Modal Reinforced Training

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    MobileCLIP2 combines DFN-trained teachers, a fine-tuned CoCa captioner, and new 5-stage FastViT variants to set state-of-the-art ImageNet-1k zero-shot accuracy at low latency.

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