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Topic: Multimodal Machine Learning Applications ×Clear all

6 claims from 4 papers

1. Computer Science › Multimodal Machine Learning Applications

   Reproducible Scaling Laws for Contrastive Language-Image Learning

   Cherti, Beaumont, Wightman et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2023

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   1. Unchecked“Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning.”

2. Computer Science › Multimodal Machine Learning Applications

   VILA: On Pre-training for Visual Language Models

   Ji, Yin, Ping, Molchanov, Shoeybi and Han · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2024

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   1. Unchecked“With an enhanced pre-training recipe we build VILA, a Visual Language model family that consistently outperforms the state-of-the-art models, e.g., LLaVA-1.5, across main benchmarks without bells and whistles.”
   2. Unchecked“Multi-modal pre-training also helps unveil appealing properties of VILA, including multi-image reasoning, enhanced in-context learning, and better world knowledge.”

3. Computer Science › Multimodal Machine Learning Applications

   Playing Lottery Tickets with Vision and Language

   Gan, Chen, Li et al. · arXiv (Cornell University) · 2021

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   1. Unchecked“However, we can find "relaxed" winning tickets at 50%-70% sparsity that maintain 99% of the full accuracy.”
   2. Unchecked“However, the highest sparsity we can achieve for ViLT is far lower than LXMERT and UNITER (30% vs. 70%).”

4. Computer Science › Multimodal Machine Learning Applications

   Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports

   Zhang, Pan, Zhong et al. · arXiv (Cornell University) · 2024

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   1. Unchecked“Conversely, GPT-series models exhibited proficiency in lesion segmentation and anatomical localization but encountered difficulties in disease diagnosis and lesion detection.”