Object Guided External Memory Network for Video Object Detection

Hanming Deng, Yang Hua, Tao Song, Zongpu Zhang, Zhengui Xue, Ruhui Ma, Neil Robertson, Haibing Guan

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Abstract

Video object detection is more challenging than image object detection because of the deteriorated frame quality. To enhance the feature representation, state-of-the-art methods propagate temporal information into the deteriorated frame by aligning and aggregating entire feature maps from multiple nearby frames. However, restricted by feature map's low storage-efficiency and vulnerable content-address allocation, long-term temporal information is not fully stressed by these methods. In this work, we propose the first object guided external memory network for online video object detection. Storage-efficiency is handled by object guided hard-attention to selectively store valuable features, and long-term information is protected when stored in addressable external data matrix. A set of read/write operations are designed to accurately propagate/allocate and delete multi-level memory feature under object guidance. We evaluate our method on the ImageNet VID dataset and achieve state-of-the-art performance as well as good speed-accuracy tradeoff. Furthermore, by visualizing the external memory, we show the detailed object-level reasoning process across frames.
Original languageEnglish
Title of host publication2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher IEEE
DOIs
Publication statusPublished - 27 Feb 2020

Publication series

Name
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

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  • Cite this

    Deng, H., Hua, Y., Song, T., Zhang, Z., Xue, Z., Ma, R., Robertson, N., & Guan, H. (2020). Object Guided External Memory Network for Video Object Detection. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) IEEE . https://doi.org/10.1109/ICCV.2019.00678