The VALUE (Video-And-Language Understanding Evaluation) benchmark is newly
introduced to evaluate and analyze multi-modal representation learning
algorithms on three video-and-language tasks: Retrieval, QA, and Captioning.
The main objective of the VALUE challenge is to train a task-agnostic model
that is simultaneously applicable for various tasks with different
characteristics. This technical report describes our winning strategies for the
VALUE challenge: 1) single model optimization, 2) transfer learning with visual
concepts, and 3) task-aware ensemble. The first and third strategies are
designed to address heterogeneous characteristics of each task, and the second
one is to leverage rich and fine-grained visual information. We provide a
detailed and comprehensive analysis with extensive experimental results. Based
on our approach, we ranked first place on the VALUE and QA phases for the
competition.

本技术报告介绍了我们在 VALUE 挑战赛中的优胜策略：单一模型优化，使用视觉概念的迁移学习以及任务感知的集成。根据我们的方法，我们在比赛的 VALUE 和 QA 阶段中排名第一。