Abstract
The convolutional neural networks have reached great achievements in solving the challenging problems of computer vision tasks, such as image classification, object detection, semantic segmentation. The core element of CNNs is the convolution operation, which gathers important features by constructing pixel relationships in a local region. Even though CNNs are universally exploited in visual feature understanding, they still have drawbacks due to that the receptive field is restrained inside local neighborhoods by the physical construction of the convolution layer. The Non-local Network introduces a novel method for modeling long-range dependencies to remedy the local neighborhood problem, which computing the correlations between the query position and all positions to capture global context features and then performing a weighted sum of the features at all positions. As a complementary part of the Non-Local Network, the proposed method called Bidirectional Non-local operation designs the bidirectional relationship, which the informative feature at a specific-query position is gathered and distributed to all positions. Notably, this work relaxes the Bidirectional Non-local complexity by simplifying the network based on the same attention maps for different query positions. To evaluate the effectiveness of the proposed method, the Bidirectional Non-local block is embedded into the backbone network of the detector Mask R-CNN. Without bells and whistles, the integrated network achieves 0.9 points higher Average Precision than Global Context Network on the major baseline.
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government. (MSIT) (2020R1A2C2008972).
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Vo, XT., Wen, L., Tran, TD., Jo, KH. (2020). Bidirectional Non-local Networks for Object Detection. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_38
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