Parallel In-context Learning for Large Vision Language Models
#in-context learning #vision-language models #parallel processing #multimodal AI #computational efficiency
📌 Key Takeaways
- Parallel in-context learning enhances large vision-language models by processing multiple examples simultaneously.
- This approach improves efficiency and scalability in handling multimodal tasks.
- It enables better generalization and adaptation to new visual and linguistic contexts.
- The method reduces computational overhead compared to sequential in-context learning.
📖 Full Retelling
arXiv:2603.16092v1 Announce Type: cross
Abstract: Large vision-language models (LVLMs) employ multi-modal in-context learning (MM-ICL) to adapt to new tasks by leveraging demonstration examples. While increasing the number of demonstrations boosts performance, they incur significant inference latency due to the quadratic computational cost of Transformer attention with respect to the context length. To address this trade-off, we propose Parallel In-Context Learning (Parallel-ICL), a plug-and-pl
🏷️ Themes
AI Efficiency, Multimodal Learning
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Original Source
arXiv:2603.16092v1 Announce Type: cross
Abstract: Large vision-language models (LVLMs) employ multi-modal in-context learning (MM-ICL) to adapt to new tasks by leveraging demonstration examples. While increasing the number of demonstrations boosts performance, they incur significant inference latency due to the quadratic computational cost of Transformer attention with respect to the context length. To address this trade-off, we propose Parallel In-Context Learning (Parallel-ICL), a plug-and-pl
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