Spatio-Temporal Graph for Video Captioning With Knowledge Distillation
Boxiao Pan, Haoye Cai, De-An Huang, Kuan-Hui Lee, Adrien Gaidon, Ehsan Adeli, Juan Carlos Niebles
Abstract
Figure 1: How to understand and describe a scene from video input? We argue that a detailed understanding of spatiotemporal object interaction is crucial for this task. In this paper, we propose a spatio-temporal graph model to explicitly capture such information for video captioning. Yellow boxes represent object proposals from Faster R-CNN [12]. Red arrows denote directed temporal edges (for clarity, only the most relevant ones are shown), while blue lines indicate undirected spatial connections. Video sample from MSVD [3] with the caption "A cat jumps into a box." Best viewed in color.
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Install the CLIlune papers fulltext 41772bdd-9bfb-42c4-9ab1-25ea90b19639Cited by top-tier papers45
- SwinBERT: End-to-End Transformers with Sparse Attention for Video CaptioningKevin Lin, Linjie Li, Chung-Ching Lin, Faisal Ahmed et al.CVPR 2022 · 263 citations
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Builds on4
- SlowFast Networks for Video RecognitionChristoph Feichtenhofer, Haoqi Fan, Jitendra Malik, Kaiming HeICCV 2019 · 4,104 citations
- Controllable Video Captioning With POS Sequence Guidance Based on Gated Fusion NetworkBairui Wang, Lin Ma, Wei Zhang, Wenhao Jiang et al.ICCV 2019 · 183 citations
- Understanding Human Gaze Communication by Spatio-Temporal Graph ReasoningLifeng Fan, Wenguan Wang, Song-Chun Zhu, Xinyu Tang et al.ICCV 2019 · 124 citations
- Joint Syntax Representation Learning and Visual Cue Translation for Video CaptioningJingyi Hou, Xinxiao Wu, Wentian Zhao, Jiebo Luo et al.ICCV 2019 · 84 citations
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