# Transferring Domain-Agnostic Knowledge in Video Question Answering Source: [Transferring Domain-Agnostic Knowledge in Video Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:41:15.837Z Authors: Tianran Wu, Noa Garcia, Mayu Otani, Chenhui Chu, Yuta Nakashima, Haruo Takemura Published: 2021-10-26T03:58:31.000Z ## Abstract Video question answering (VideoQA) is designed to answer a given question based on a relevant video clip. The current available large-scale datasets have made it possible to formulate VideoQA as the joint understanding of visual and language information. However, this training procedure is costly and still less competent with human performance. In this paper, we investigate a transfer learning method by the introduction of domain-agnostic knowledge and domain-specific knowledge. First, we develop a novel transfer learning framework, which finetunes the pre-trained model by applying domain-agnostic knowledge as the medium. Second, we construct a new VideoQA dataset with 21,412 human-generated question-answer samples for comparable transfer of knowledge. Our experiments show that: (i) domain-agnostic knowledge is transferable and (ii) our proposed transfer learning framework can boost VideoQA performance effectively. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()