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Recursive Context Routing for Object Detection

Abstract

Recent studies have confirmed that modeling contexts is important for object detection. However, current context modeling approaches still have limited expressive capacity and dynamics to encode contextual relationships and model contexts, deteriorating their effectiveness. In this paper, we instead seek to recast the current context modeling framework and perform more dynamic context modeling for object detection. In particular, we devise a novel Recursive Context Routing (ReCoR) mechanism to encode contextual relationships and model contexts more effectively. The ReCoR progressively models more contexts through a recursive structure, providing a more feasible and more comprehensive method to utilize complicated contexts and contextual relationships. For each recursive stage, we further decompose the modeling of contexts and contextual relationships into a spatial modeling process and a channel-wise modeling process, avoiding the need for exhaustive modeling of all the potential pair-wise contextual relationships with more dynamics in a single pass. The spatial modeling process focuses on spatial contexts and gradually involves more spatial contexts according to the recursive architecture. In the channel-wise modeling process, we introduce a context routing algorithm to improve the efficacy of modeling channel-wise contextual relationships dynamically. We perform a comprehensive evaluation of the proposed ReCoR on the popular MS COCO dataset and PASCAL VOC dataset. The effectiveness of the ReCoR can be validated on both datasets according to the consistent performance gains of applying our method on different baseline object detectors. For example, on MS COCO dataset, our approach can respectively deliver around 10% relative improvements for a Mask RCNN detector on the bounding box task, and 7% relative improvements on the instance segmentation task, surpassing existing context modeling approaches with a great margin. State-of-the-art detection performance can also be accessed by applying the ReCoR on the Cascade Mask RCNN detector, illustrating the great benefits of our method for improving context modeling and object detection.

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Notes

  1. http://cocodataset.org/.

  2. https://github.com/open-mmlab/mmdetection.

  3. FLOPs: floating point operations.

  4. GMAC:giga multiply-accumulate operations per second.

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Correspondence to Jing Zhang or Dacheng Tao.

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Communicated by Vittorio Ferrari.

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This work was supported by Australian Research Council Projects FL-170100117, DP-180103424, IH-180100002, IC-190100031, LE-200100049.

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Chen, Z., Zhang, J. & Tao, D. Recursive Context Routing for Object Detection. Int J Comput Vis 129, 142–160 (2021). https://doi.org/10.1007/s11263-020-01370-7

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Keywords

  • Object detection
  • Context modeling
  • Computer vision
  • Deep learning