Abstract
Models based on transformer architecture have risen to prominence for video captioning. However, most models are only to improve either the encoder or the decoder, because when we improve the encoder and decoder simultaneously, the shortcomings of either side may be amplified. Based on the transformer architecture, we connect a bidirectional decoder and an encoder that integrates fine-grained spatio-temporal features, objects, and relationships between the objects in the video. Experiments show that improvements in the encoder amplify the information leakage of the bidirectional decoder and further produce a worse result. To tackle this problem, we generate pseudo reverse captions and propose a Bidirectional Transformer with Knowledge Graph (BTKG), which integrates the outputs of two encoders into the forward and backward decoders of the bidirectional decoder, respectively. In addition, we make fine-grained improvements on the interior of the different encoders according to four modal features of the video. Experiments on two mainstream benchmark datasets, i.e., MSVD and MSR-VTT, demonstrate the effectiveness of BTKG, which achieves state-of-the-art performance in significant metrics. Moreover, the sentences generated by BTKG contain scene words and modifiers, that are more in line with human language habits. Codes are available on https://github.com/nickchen121/BTKG.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2625–2634
Venugopalan S, Xu H, Donahue J, Rohrbach M, Mooney R, Saenko K (2014) Translating videos to natural language using deep recurrent neural networks. arXiv:1412.4729
Yao L, Torabi A, Cho K, Ballas N, Pal C, Larochelle H, Courville A (2015) Describing videos by exploiting temporal structure. In: Proceedings of the IEEE international conference on computer vision, pp 4507–4515
Xu J, Yao T, Zhang Y, Mei T (2017) Learning multimodal attention LSTM networks for video captioning. In: Proceedings of the 25th ACM international conference on Multimedia, pp 537–545
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Polosukhin I (2017) Attention is all you need. Advan Neural Inform Process Syst, 30
Zhou L, Zhou Y, Corso JJ, Socher R, Xiong C (2018) End-to-end dense video captioning with masked transformer. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8739–8748
Zhong M, Zhang H, Xiong H, Chen Y, Wang M. Zhou X Kgvideo: a video captioning method based on object detection and knowledge graph. Available at SSRN 4017055
Zhong M, Zhang H, Wang Y, Xiong H (2022) BiTransformer: augmenting semantic context in video captioning via bidirectional decoder. Mach Vis Appl 33(5):1–9
Hori C, Hori T, Lee TY, Zhang Z, Harsham B, Hershey JR, Sumi K (2017) Attention-based multimodal fusion for video description. In: Proceedings of the IEEE international conference on computer vision, pp 4193–4202
Wang S, Zhou T, Lu Y, Di H (2022) Detail-preserving transformer for light field image super-resolution. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, no 3, pp 2522–2530
Liang C, Wang W, Zhou T, Yang Y (2022) Visual abductive reasoning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 15565–15575
Aafaq N, Akhtar N, Liu W, Gilani SZ, Mian A (2019) Spatio-temporal dynamics and semantic attribute enriched visual encoding for video captioning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12487–12496
He K, Gkioxari G, Dolläir P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969
Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Advan Neural Inform Process Syst, 26
Zhou L, Zhang J, Zong C (2019) Synchronous bidirectional neural machine translation. Trans Assoc Comput Linguistics 7:91–105
Zhou L, Zhang J, Zong C (2019) Synchronous bidirectional neural machine translation. Trans Assoc Comput Linguistics 7:91–105
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advan Neural Inform Process Syst, 25
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Zhong M, Chen Y, Zhang H, Xiong H, Wang Z (2023) Multimodal-enhanced hierarchical attention network for video captioning. Multimedia Syst, 1–14
Song J, Guo Z, Gao L, Liu W, Zhang D, Shen HT (2017) Hierarchical LSTM with adjusted temporal attention for video captioning. arXiv:1706.01231
Ji S, Xu W, Yang M, Yu K (2012) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231
Anderson P, He X, Buehler C, Teney D, Johnson M, Gould S, Zhang L (2018) Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6077–6086
Yan C, Tu Y, Wang X, Zhang Y, Hao X, Zhang Y, Dai Q (2019) STAT: Spatial-temporal attention mechanism for video captioning. IEEE Trans Multimedia 22(1):229–241
Zhou T, Li J, Wang S, Tao R, Shen J (2020) Matnet: Motion-attentive transition network for zero-shot video object segmentation. IEEE Trans Image Process 29:8326–8338
Liu F, Ren X, Wu X, Yang B, Ge S, Sun X (2021) O2NA: an object-oriented non-autoregressive approach for controllable video captioning. arXiv:2108.02359
Xu W, Yu J, Miao Z, Wan L, Tian Y, Ji Q (2020) Deep reinforcement polishing network for video captioning. IEEE Trans Multimedia 23:1772–1784
Yang B, Zou Y, Liu F, Zhang C (2019) Non-autoregressive coarse-to-fine video captioning. arXiv:1911.12018
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence
Carreira J, Zisserman A (2017) Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp 6299–6308
Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, Zisserman A (2017) The kinetics human action video dataset. arXiv:1705.06950
Han X, Cao S, Lv X, Lin Y, Liu Z, Sun M, Li J (2018) Openke: an open toolkit for knowledge embedding. In: Proceedings of the 2018 conference on empirical methods in natural language processing: system demonstrations, pp 139–144
Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Zitnick CL (2014) Microsoft coco: common objects in context. European conference on computer vision. Springer, Cham, pp 740–755
Lin TY, Dolláir P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125
Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Advan Neural Inform Process Syst, 28
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv:1301.3781
Chen D, Dolan WB (2011) Collecting highly parallel data for paraphrase evaluation. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 190–200
Xu J, Mei T, Yao T, Rui Y (2016) Msr-vtt: a large video description dataset for bridging video and language. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5288–5296
Pei W, Zhang J, Wang X, Ke L, Shen X, Tai YW (2019) Memory-attended recurrent network for video captioning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8347–8356
Pan B, Cai H, Huang DA, Lee KH, Gaidon A, Adeli E, Niebles JC (2020) Spatio-temporal graph for video captioning with knowledge distillation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10870–10879
Wang, B., Ma, L., Zhang, W., & Liu, W. (2018). Reconstruction network for video captioning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7622-7631)
Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543
Papineni K, Roukos S, Ward T, Zhu WJ (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the association for computational linguistics, pp 311–318
Banerjee S, Lavie A (2005) METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pp 65–72
Vedantam R, Lawrence Zitnick C, Parikh D (2015) Cider: consensus-based image description evaluation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4566–4575
Lin CY (2004) Rouge: a package for automatic evaluation of summaries. In: Text summarization branches out, pp 74–81
Yan C, Tu Y, Wang X, Zhang Y, Hao X, Zhang Y, Dai Q (2019) STAT: spatial-temporal attention mechanism for video captioning. IEEE Trans Multimedia 22(1):229–241
Novikova J, Dušiek O, Curry AC, Rieser V (2017) Why we need new evaluation metrics for NLG. arXiv:1707.06875
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980
Chen Y, Wang S, Zhang W, Huang Q (2018) Less is more: picking informative frames for video captioning. In: Proceedings of the European conference on computer vision (ECCV), pp 358–373
Xu W, Yu J, Miao Z, Wan L, Tian Y, Ji Q (2020) Deep reinforcement polishing network for video captioning. IEEE Trans Multimedia 23:1772–1784
Zhang Z, Shi Y, Yuan C, Li B, Wang P, Hu W, Zha ZJ (2020). Object relational graph with teacher-recommended learning for video captioning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 13278–13288
Ryu H, Kang S, Kang H, Yoo CD (2021) Semantic grouping network for video captioning. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, no 3, pp 2514–2522
Chen S, Jiang YG (2021) Motion guided region message passing for video captioning. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 1543–1552
Vaidya J, Subramaniam A, Mittal A (2022) Co-Segmentation Aided Two-Stream Architecture for Video Captioning. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 2774–2784
Wu B, Niu G, Yu J, Xiao X, Zhang J, Wu H (2022) Towards Knowledge-aware Video Captioning via Transitive Visual Relationship Detection. IEEE Trans Circuits Syst Video Technol
Sulem E, Abend O, Rappoport A (2018) Bleu is not suitable for the evaluation of text simplification. arXiv:1810.05995
Acknowledgements
The authors would like to thank the anonymous reviewers for their constructive comments to improve the paper. The research is partially supported by the National Natural Science Foundation (NNSF) of China (No. 61877031) and Jiangxi Normal University Graduate Innovation Fund (YJS2022029).
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Zhong, M., Chen, Y., Zhang, H. et al. Bidirectional transformer with knowledge graph for video captioning. Multimed Tools Appl 83, 58309–58328 (2024). https://doi.org/10.1007/s11042-023-17822-4
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DOI: https://doi.org/10.1007/s11042-023-17822-4