VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding
Kangsan Kim, Geon Park, Youngwan Lee, Woongyeong Yeo, Sung Ju Hwang
2024-12-05
Summary
This paper presents VideoICL, a new framework designed to improve video understanding for tasks that involve videos not seen during training, known as out-of-distribution (OOD) tasks.
What's the problem?
While recent advancements in video models have improved their ability to understand and analyze videos, these models often struggle when faced with OOD tasks. These tasks involve scenarios or content that were not included in the training data, leading to a drop in performance. Traditional methods to fix this issue, like fine-tuning the model on new data, can be very resource-intensive and impractical.
What's the solution?
To tackle these challenges, VideoICL introduces a method that uses in-context learning (ICL) without needing to retrain the model. It selects the most relevant examples from previously seen data based on similarity to the current task. If the model's initial response lacks confidence, it iteratively refines its answer by selecting new examples and trying again until it reaches a satisfactory level of confidence. This process allows the model to effectively utilize a larger pool of examples while managing its limited context length.
Why it matters?
This research is important because it enhances the ability of AI systems to understand videos in diverse and unpredictable situations without requiring extensive retraining. By improving performance on OOD video tasks, VideoICL can lead to better applications in areas like autonomous driving, surveillance, and content creation, where accurate video analysis is crucial.
Abstract
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