Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling

• URL: http://arxiv.org/abs/2210.03941v1
• Date: Sat, 8 Oct 2022 07:03:31 GMT
• Title: Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling
• Authors: Hsin-Ying Lee, Hung-Ting Su, Bing-Chen Tsai, Tsung-Han Wu, Jia-Fong Yeh, Winston H. Hsu
• Abstract summary: We decouple spatial-temporal modeling and integrate an image- and a video-language to learn fine-grained visual understanding. We propose a novel pre-training objective, Temporal Referring Modeling, which requires the model to identify temporal positions of events in video sequences. Our model outperforms previous work pre-trained on orders of magnitude larger datasets.
• Score: 28.530765643908083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained than that of image-language models; existing practices of temporal modeling also suffer from weak and noisy alignment between modalities. To learn fine-grained visual understanding, we decouple spatial-temporal modeling and propose a hybrid pipeline, Decoupled Spatial-Temporal Encoders, integrating an image- and a video-language encoder. The former encodes spatial semantics from larger but sparsely sampled frames independently of time, while the latter models temporal dynamics at lower spatial but higher temporal resolution. To help the video-language model learn temporal relations for video QA, we propose a novel pre-training objective, Temporal Referring Modeling, which requires the model to identify temporal positions of events in video sequences. Extensive experiments demonstrate that our model outperforms previous work pre-trained on orders of magnitude larger datasets.

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