Papers with Vision-Language

2 papers
Enhancing Advanced Visual Reasoning Ability of Large Language Models (2024.emnlp-main)

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Challenge: Recent advances in Vision-Language (VL) research have sparked new benchmarks for complex visual reasoning, challenging models’ advanced reasoning ability.
Approach: They propose a novel multi-modal in-context learning methodology to enhance LLMs’ contextual understanding and reasoning.
Outcome: The proposed model achieves SOTA performance among all visual reasoning tasks and achieves a 'higher level of accuracy' than previous models.
MAGMA – Multimodal Augmentation of Generative Models through Adapter-based Finetuning (2022.findings-emnlp)

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Challenge: Large-scale pretraining is becoming the norm in Vision-Language (VL) modeling.
Approach: They propose a method for augmenting generative language models with additional modalities using adapter-based finetuning.
Outcome: The proposed method outperforms Frozen on open-ended generative tasks while maintaining the language model weights.

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