VideoICL: Confidence-based Iterative In-context Learning for
  Out-of-Distribution Video Understanding
Authors: name: Kangsan Kim, name: Geon Park, name: Youngwan Lee, name: Woongyeong Yeo, name: Sung Ju Hwang

Recent advancements in video large multimodal models (LMMs) have
significantly improved their video understanding and reasoning capabilities.
However, their performance drops on out-of-distribution (OOD) tasks that are
underrepresented in training data. Traditional methods like fine-tuning on OOD
datasets are impractical due to high computational costs. While In-context
learning (ICL) with demonstration examples has shown promising generalization
performance in language tasks and image-language tasks without fine-tuning,
applying ICL to video-language tasks faces challenges due to the limited
context length in Video LMMs, as videos require longer token lengths. To
address these issues, we propose VideoICL, a novel video in-context learning
framework for OOD tasks that introduces a similarity-based relevant example
selection strategy and a confidence-based iterative inference approach. This
allows to select the most relevant examples and rank them based on similarity,
to be used for inference. If the generated response has low confidence, our
framework selects new examples and performs inference again, iteratively
refining the results until a high-confidence response is obtained. This
approach improves OOD video understanding performance by extending effective
context length without incurring high costs. The experimental results on
multiple benchmarks demonstrate significant performance gains, especially in
domain-specific scenarios, laying the groundwork for broader video
comprehension applications. Code will be released at
https://github.com/KangsanKim07/VideoICL