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The VALUE (Video-And-Language Understanding Evaluation) benchmark is newlyintroduced to evaluate and analyze multi-modal representation learningalgorithms on three video-and-language tasks: Retrieval, QA, and Captioning.The main objective of the VALUE challenge is to train a task-agnostic modelthat is simultaneously applicable for various tasks with differentcharacteristics. This technical report describes our winning strategies for theVALUE challenge: 1) single model optimization, 2) transfer learning with visualconcepts, and 3) task-aware ensemble. The first and third strategies aredesigned to address heterogeneous characteristics of each task, and the secondone is to leverage rich and fine-grained visual information. We provide adetailed and comprehensive analysis with extensive experimental results. Basedon our approach, we ranked first place on the VALUE and QA phases for thecompetition.

Winning the ICCV'2021 VALUE Challenge: Task-aware Ensemble and Transfer Learning with Visual Concepts