MAVT-FG: Multimodal Audio-Visual Transformer for Weakly-supervised Fine-Grained Recognition
Pages 3811 - 3819
Abstract
Weakly-supervised fine-grained recognition aims to detect potential differences between subcategories at a more detailed scale without using any manual annotations. While most recent works focus on classical image-based fine-grained recognition that recognizes subcategories at image-level, video-based fine-grained recognition is much more challenging and specifically needed. In this paper, we propose a Multimodal Audio-Visual Transformer for Weakly-supervised Fine-Grained Recognition (MAVT-FG) model which incorporates audio-visual modalities. Specifically, MAVT-FG consists of Audio-Visual Dual-Encoder for feature extraction, Cross-Decoder for Audio-Visual Fusion (DAVF) to exploit inherent cues and correspondences between two modalities, and Search-and-Select Fine-grained Branch (SSFG) to capture the most discriminative regions. Furthermore, we construct a new benchmark: Fine-grained Birds of Audio-Visual (FGB-AV) for audio-visual weakly-supervised fine-grained recognition at video-level. Experimental results show that our method achieves superior performance and outperforms other state-of-the-art methods.
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References
[1]
Panagiotis Antoniadis, Ioannis Pikoulis, Panagiotis P Filntisis, and Petros Maragos. 2021. An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 3645--3651.
[2]
Yusuf Aytar, Carl Vondrick, and Antonio Torralba. 2016. Soundnet: Learning sound representations from unlabeled video. Advances in neural information processing systems, Vol. 29 (2016).
[3]
birder.cn. 2017. birder. http://www.birder.cn/video.html.
[4]
Birdsdata.com. 2020. Birdsdata. https://open.baai.ac.cn/data-set-detail/.
[5]
Forrest Briggs, Xiaoli Z Fern, and Raviv Raich. 2012. Rank-loss support instance machines for MIML instance annotation. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. 534--542.
[6]
Dongliang Chang, Yifeng Ding, Jiyang Xie, Ayan Kumar Bhunia, Xiaoxu Li, Zhanyu Ma, Ming Wu, Jun Guo, and Yi-Zhe Song. 2020. The devil is in the channels: Mutual-channel loss for fine-grained image classification. IEEE Transactions on Image Processing, Vol. 29 (2020), 4683--4695.
[7]
Yanbei Chen, Yongqin Xian, A Koepke, Ying Shan, and Zeynep Akata. 2021. Distilling audio-visual knowledge by compositional contrastive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7016--7025.
[8]
Ying Cheng, Ruize Wang, Zhihao Pan, Rui Feng, and Yuejie Zhang. 2020. Look, listen, and attend: Co-attention network for self-supervised audio-visual representation learning. In Proceedings of the 28th ACM International Conference on Multimedia. 3884--3892.
[9]
Ruoyi Du, Dongliang Chang, Ayan Kumar Bhunia, Jiyang Xie, Zhanyu Ma, Yi-Zhe Song, and Jun Guo. 2020. Fine-grained visual classification via progressive multi-granularity training of jigsaw patches. In European Conference on Computer Vision. Springer, 153--168.
[10]
Yu Gao, Xintong Han, Xun Wang, Weilin Huang, and Matthew Scott. 2020. Channel interaction networks for fine-grained image categorization. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 10818--10825.
[11]
Yuan Gong, Yu-An Chung, and James Glass. 2021. Ast: Audio spectrogram transformer. arXiv preprint arXiv:2104.01778 (2021).
[12]
Ning Han, Jingjing Chen, Guangyi Xiao, Hao Zhang, Yawen Zeng, and Hao Chen. 2021. Fine-grained Cross-modal Alignment Network for Text-Video Retrieval. In Proceedings of the 29th ACM International Conference on Multimedia. 3826--3834.
[13]
Ju He and Adam Kortylewski Cheng Yang Yutong Bai Changhu Wang Alan Yuille Jieneng Chen, Shuai Liu. 2021. TransFG: A Transformer Architecture for Fine-grained Recognition. arXiv (2021).
[14]
Xiangteng He, Yuxin Peng, and Liu Xie. 2019. A new benchmark and approach for fine-grained cross-media retrieval. In Proceedings of the 27th ACM International Conference on Multimedia. 1740--1748.
[15]
Nicholas P Holmes and Charles Spence. 2005. Multisensory integration: space, time and superadditivity. Current Biology, Vol. 15, 18 (2005), R762--R764.
[16]
Yunqing Hu, Xuan Jin, Yin Zhang, Haiwen Hong, Jingfeng Zhang, Yuan He, and Hui Xue. 2021. RAMS-Trans: Recurrent Attention Multi-scale Transformer for Fine-grained Image Recognition. In Proceedings of the 29th ACM International Conference on Multimedia. 4239--4248.
[17]
Soonil Kwon. 2019. A CNN-assisted enhanced audio signal processing for speech emotion recognition. Sensors, Vol. 20, 1 (2019), 183.
[18]
Yan-Bo Lin and Yu-Chiang Frank Wang. 2020. Audiovisual transformer with instance attention for audio-visual event localization. In Proceedings of the Asian Conference on Computer Vision.
[19]
Xinda Liu, Lili Wang, and Xiaoguang Han. 2021. Transformer with peak suppression and knowledge guidance for fine-grained image recognition. arXiv preprint arXiv:2107.06538 (2021).
[20]
ManyBirds and Malcolm Mark Swan. 2006. Manybirds. https://www.manybirds.com/.
[21]
Mathew Monfort, SouYoung Jin, Alexander Liu, David Harwath, Rogerio Feris, James Glass, and Aude Oliva. 2021. Spoken moments: Learning joint audio-visual representations from video descriptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14871--14881.
[22]
Pedro Morgado, Nuno Vasconcelos, and Ishan Misra. 2021. Audio-visual instance discrimination with cross-modal agreement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12475--12486.
[23]
Cornell Lab of Ornithology. 2022. allaboutbirds. https://www.aboutbirds.org/.
[24]
Juan-Manuel Pérez-Rúa, Valentin Vielzeuf, Stéphane Pateux, Moez Baccouche, and Frédéric Jurie. 2019. Mfas: Multimodal fusion architecture search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6966--6975.
[25]
Xiaoye Qu, Pengwei Tang, Zhikang Zou, Yu Cheng, Jianfeng Dong, Pan Zhou, and Zichuan Xu. 2020. Fine-grained iterative attention network for temporal language localization in videos. In Proceedings of the 28th ACM International Conference on Multimedia. 4280--4288.
[26]
Mirco Ravanelli, Titouan Parcollet, and Yoshua Bengio. 2019. The Pytorch-kaldi Speech Recognition Toolkit. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 6465--6469. https://doi.org/10.1109/ICASSP.2019.8683713
[27]
Nikitha Sharma, Aditi Vijayeendra, Vishnu Gopakumar, Prakhar Patni, and Ashwini Bhat. 2022. Automatic Identification of Bird Species using Audio/Video Processing. In 2022 International Conference for Advancement in Technology (ICONAT). IEEE, 1--6.
[28]
Liang Sun, Xiang Guan, Yang Yang, and Lei Zhang. 2020. Text-Embedded Bilinear Model for Fine-Grained Visual Recognition. In Proceedings of the 28th ACM International Conference on Multimedia. 211--219.
[29]
Z. Sun, Y. Yao, X. S. Wei, Y. Zhang, F. Shen, J. Wu, J. Zhang, and H. T. Shen. 2021. Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach. (2021).
[30]
Ruijie Tao, Zexu Pan, Rohan Kumar Das, Xinyuan Qian, Mike Zheng Shou, and Haizhou Li. 2021. Is someone speaking? exploring long-term temporal features for audio-visual active speaker detection. In Proceedings of the 29th ACM International Conference on Multimedia. 3927--3935.
[31]
Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, and Serge Belongie. 2015. Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 595--604.
[32]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. arXiv (2017).
[33]
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. 2011. The caltech-ucsd birds-200--2011 dataset. (2011).
[34]
Jiahao Wang, Yunhong Wang, Sheng Liu, and Annan Li. 2021. Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning. In Proceedings of the 29th ACM International Conference on Multimedia. 582--591.
[35]
Shijie Wang, Zhihui Wang, Haojie Li, and Wanli Ouyang. 2020c. Category-specific semantic coherency learning for fine-grained image recognition. In Proceedings of the 28th ACM International Conference on Multimedia. 174--183.
[36]
Weiyao Wang, Du Tran, and Matt Feiszli. 2020b. What makes training multi-modal classification networks hard?. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 12695--12705.
[37]
Xin Wang, Wei Huang, Qi Liu, Yu Yin, Zhenya Huang, Le Wu, Jianhui Ma, and Xue Wang. 2020a. Fine-grained similarity measurement between educational videos and exercises. In Proceedings of the 28th ACM International Conference on Multimedia. 331--339.
[38]
Xing Xu, Kaiyi Lin, Lianli Gao, Huimin Lu, Heng Tao Shen, and Xuelong Li. 2022a. Learning Cross-Modal Common Representations by Private-Shared Subspaces Separation. IEEE Trans. Cybern., Vol. 52, 5 (2022), 3261--3275.
[39]
Xing Xu, Kaiyi Lin, Yang Yang, Alan Hanjalic, and Heng Tao Shen. 2022b. Joint Feature Synthesis and Embedding: Adversarial Cross-Modal Retrieval Revisited. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 44, 6 (2022), 3030--3047.
[40]
Xing Xu, Tan Wang, Yang Yang, Lin Zuo, Fumin Shen, and Heng Tao Shen. 2020. Cross-Modal Attention With Semantic Consistence for Image-Text Matching. IEEE Trans. Neural Networks Learn. Syst., Vol. 31, 12 (2020), 5412--5425.
[41]
Yazhou Yao, Xiansheng Hua, Guanyu Gao, Zeren Sun, Zhibin Li, and Jian Zhang. 2020. Bridging the web data and fine-grained visual recognition via alleviating label noise and domain mismatch. In Proceedings of the 28th ACM International Conference on Multimedia. 1735--1744.
[42]
Jingran Zhang, Xing Xu, Fumin Shen, Huimin Lu, Xin Liu, and Heng Tao Shen. 2021b. Enhancing audio-visual association with self-supervised curriculum learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 3351--3359.
[43]
Su Zhang, Yi Ding, Ziquan Wei, and Cuntai Guan. 2021a. Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 3567--3574.
[44]
Qijie Zhao, Tao Sheng, Yongtao Wang, Zhi Tang, Ying Chen, Ling Cai, and Haibin Ling. 2019. M2det: A single-shot object detector based on multi-level feature pyramid network. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 9259--9266.
[45]
Mohan Zhou, Yalong Bai, Wei Zhang, Tiejun Zhao, and Tao Mei. 2020. Look-into-object: Self-supervised structure modeling for object recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11774--11783.
[46]
Peiqin Zhuang, Yali Wang, and Yu Qiao. 2020. Learning attentive pairwise interaction for fine-grained classification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 13130--13137.
Index Terms
- MAVT-FG: Multimodal Audio-Visual Transformer for Weakly-supervised Fine-Grained Recognition
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Published In
October 2022
7537 pages
ISBN:9781450392037
DOI:10.1145/3503161
- General Chairs:
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- Alberto del Bimbo,
- Shin'ichi Satoh,
- Nicu Sebe,
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- Qin Jin,
- Vincent Oria,
- Laura Toni
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Published: 10 October 2022
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