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Deep Radial Embedding for Visual Sequence Learning

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13666))

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Abstract

Connectionist Temporal Classification (CTC) is a popular objective function in sequence recognition, which provides supervision for unsegmented sequence data through aligning sequence and its corresponding labeling iteratively. The blank class of CTC plays a crucial role in the alignment process and is often considered responsible for the peaky behavior of CTC. In this study, we propose an objective function named RadialCTC that constrains sequence features on a hypersphere while retaining the iterative alignment mechanism of CTC. The learned features of each non-blank class are distributed on a radial arc from the center of the blank class, which provides a clear geometric interpretation and makes the alignment process more efficient. Besides, RadialCTC can control the peaky behavior by simply modifying the logit of the blank class. Experimental results of recognition and localization demonstrate the effectiveness of RadialCTC on two sequence recognition applications.

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Acknowledgement

This study was partially supported by the Natural Science Foundation of China under contract No. 61976219.

Author information

Authors and Affiliations

  1. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China

    Yuecong Min, Peiqi Jiao & Xilin Chen

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yuecong Min, Peiqi Jiao & Xilin Chen

  3. Xiaomi Inc., Beijing, China

    Yanan Li, Xiaotao Wang & Lei Lei

  4. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Xiujuan Chai

Authors
  1. Yuecong Min
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  2. Peiqi Jiao
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  3. Yanan Li
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  7. Xilin Chen
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Corresponding author

Correspondence to Xilin Chen .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Min, Y. et al. (2022). Deep Radial Embedding for Visual Sequence Learning. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13666. Springer, Cham. https://doi.org/10.1007/978-3-031-20068-7_14

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  • DOI: https://doi.org/10.1007/978-3-031-20068-7_14

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