4 papers
Parallel In-context Learning for Large Vision Language Models
Shin'ya Yamaguchi, Daiki Chijiwa, Tamao Sakao +1
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 demo…
Lossless Vocabulary Reduction for Auto-Regressive Language Models
Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida +4
Tokenization -- the process of decomposing a given text into a sequence of subwords called tokens -- is one of the key components in the development of language models. Particularl…
MultiModal Fine-tuning with Synthetic Captions
Shohei Enomoto, Shin'ya Yamaguchi
In this paper, we address a fundamental gap between pre-training and fine-tuning of deep neural networks: while pre-training has shifted from unimodal to multimodal learning with e…
Difference Vector Equalization for Robust Fine-tuning of Vision-Language Models
Satoshi Suzuki, Shin'ya Yamaguchi, Shoichiro Takeda +7
Contrastive pre-trained vision-language models, such as CLIP, demonstrate strong generalization abilities in zero-shot classification by leveraging embeddings extracted from image…