核心概念
Textual information plays a crucial role in improving the performance of multimodal in-context learning, both in unsupervised and supervised retrieval of in-context examples.
摘要
The content explores the impact of textual information on the retrieval of in-context examples for multimodal in-context learning (M-ICL). The key insights are:
-
Unsupervised Retrieval:
- The authors conduct a comprehensive analysis on the role of textual information in unsupervised retrieval of in-context examples for M-ICL.
- They compare different configurations of unsupervised retrievers, including those that only use image information (Q-I-M-I) and those that incorporate both image and text (Q-I-M-IT).
- The results show that the inclusion of textual information leads to significant improvements in M-ICL performance across various numbers of in-context examples.
-
Supervised Retrieval:
- The authors propose a novel Multimodal Supervised In-context Examples Retrieval (MSIER) framework that leverages both visual and textual information to select the most relevant in-context examples.
- MSIER outperforms the unsupervised approaches, demonstrating the benefits of a supervised retrieval mechanism tailored for M-ICL.
- The authors investigate the impact of textual information during the training and evaluation of the MSIER model, revealing that incorporating text data in the training process is crucial for the model's effectiveness.
-
Extensive Experiments:
- The proposed methods are evaluated on three representative multimodal tasks: image captioning, visual question answering, and rank classification.
- The results show that the MSIER method achieves the best performance, highlighting the importance of strategic selection of in-context examples for enhancing M-ICL capabilities.
- The authors also provide insights into the transferability of the supervised retriever across different datasets and language models, demonstrating the generalizability of their approach.
Overall, the content emphasizes the significant impact of textual information on the retrieval of in-context examples for multimodal in-context learning, and introduces a novel supervised retrieval framework that effectively leverages both visual and textual modalities.
統計資料
A restaurant has modern wooden tables and chairs.
Some very big pretty birds in some tall grass.
引述
"The increase in parameter size of multimodal large language models (MLLMs) introduces significant capabilities, particularly in-context learning, where MLLMs enhance task performance without updating pre-trained parameters."
"Our study offers an in-depth evaluation of the impact of textual information on the unsupervised selection of in-context examples in multimodal contexts, uncovering a notable sensitivity of retriever performance to the employed modalities."
"Responding to this, we introduce a novel supervised MLLM-retriever MSIER that employs a neural network to select examples that enhance multimodal in-context learning efficiency."