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International Journal of Computer Vision

Erschienen in:

19.08.2020

Recursive Context Routing for Object Detection

verfasst von: Zhe Chen, Jing Zhang, Dacheng Tao

Erschienen in: International Journal of Computer Vision | Ausgabe 1/2021

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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.

Vorheriger Artikel Temporally Coherent General Dynamic Scene Reconstruction

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http://cocodataset.org/.

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

FLOPs: floating point operations.

GMAC:giga multiply-accumulate operations per second.

Auckland, M. E., Cave, K. R., & Donnelly, N. (2007). Nontarget objects can influence perceptual processes during object recognition. Psychonomic Bulletin Review, 14(2), 332–337.CrossRef

Bell, S., Lawrence Zitnick, C., Bala, K., & Girshick, R. (2016). Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. In CVPR (pp. 2874–2883). IEEE.

Biederman, I., Rabinowitz, J. C., Glass, A. L., & Stacy, E. W. (1974). On the information extracted from a glance at a scene. Journal of Experimental Psychology, 103(3), 597.CrossRef

Biederman, I., Mezzanotte, R. J., & Rabinowitz, J. C. (1982). Scene perception: Detecting and judging objects undergoing relational violations. Cognitive Psychology, 14(2), 143–177.CrossRef

Boyce, S. J., Pollatsek, A., & Rayner, K. (1989). Effect of background information on object identification. Journal of Experimental Psychology: Human Perception and Performance, 15(3), 556.

Brockmole, J. R., Castelhano, M. S., & Henderson, J. M. (2006). Contextual cueing in naturalistic scenes: Global and local contexts. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(4), 699.

Brockmole, J. R., Hambrick, D. Z., Windisch, D. J., & Henderson, J. M. (2008). The role of meaning in contextual cueing: Evidence from chess expertise. The Quarterly Journal of Experimental Psychology, 61(12), 1886–1896.CrossRef

Cai, Z., & Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. In CVPR. IEEE.

Cao, Y., Xu, J., Lin, S., Wei, F., & Hu, H. (2019). Gcnet: Non-local networks meet squeeze-excitation networks and beyond. arXiv preprint arXiv:1904.11492.

Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., et al. (2019a). Hybrid task cascade for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4974–4983).

Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017a). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.CrossRef

Chen, L. C., Papandreou, G., Schroff, F., & Adam, H. (2017b). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587.

Chen, X., & Gupta, A. (2017). Spatial memory for context reasoning in object detection. In ICCV (pp. 4106–4116). IEEE.

Chen, X., Li, L. J., Fei-Fei, L., & Gupta, A. (2018a). Iterative visual reasoning beyond convolutions. In CVPR. IEEE.

Chen, Y., Rohrbach, M., Yan, Z., Shuicheng, Y., Feng, J., & Kalantidis, Y. (2019b). Graph-based global reasoning networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 433–442).

Chen, Z., Huang, S., & Tao, D. (2018b). Context refinement for object detection. In Proceedings of the European conference on computer vision (ECCV) (pp. 71–86). Springer, Berlin.

Choi, M. J., Lim, J. J., Torralba, A., & Willsky, A. S. (2010). Exploiting hierarchical context on a large database of object categories. In: CVPR (pp. 129–136). IEEE.

Chun, M. M., & Jiang, Y. (1998). Contextual cueing: Implicit learning and memory of visual context guides spatial attention. Cognitive Psychology, 36(1), 28–71.MathSciNetCrossRef

Chun, M. M., & Jiang, Y. (1999). Top-down attentional guidance based on implicit learning of visual covariation. Psychological Science, 10(4), 360–365.CrossRef

Chun, M. M., & Jiang, Y. (2003). Implicit, long-term spatial contextual memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(2), 224.

Dai, J., Li, Y., He, K., & Sun, J. (2016). R-fcn: Object detection via region-based fully convolutional networks. In NIPS (pp. 379–387).

Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., et al. (2017). Deformable convolutional networks. In ICCV. IEEE.

Davenport, J. L., & Potter, M. C. (2004). Scene consistency in object and background perception. Psychological Science, 15(8), 559–564.CrossRef

De Graef, P., De Troy, A., & d’Ydewalle, G. (1992). Local and global contextual constraints on the identification of objects in scenes. Canadian Journal of Psychology/Revue canadienne de psychologie, 46(3), 489.CrossRef

Divvala, S. K., Hoiem, D., Hays, J. H., Efros, A. A., & Hebert, M. (2009). An empirical study of context in object detection. In CVPR (pp. 1271–1278). IEEE.

Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2007). The pascal visual object classes challenge 2007 (voc2007) results.

Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge (Vol. 88, pp. 303–338). Berlin: Springer.

Galleguillos, C., & Belongie, S. (2010). Context based object categorization: A critical survey. CVIU, 114(6), 712–722.

Galleguillos, C., Rabinovich, A., & Belongie, S. (2008). Object categorization using co-occurrence, location and appearance. In CVPR (pp. 1–8). IEEE.

Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013). Vision meets robotics: The kitti dataset. IJRR, 32, 1231–1237.

Ghiasi, G., Lin, T. Y., & Le, Q. V. (2018). Dropblock: A regularization method for convolutional networks. In Advances in neural information processing systems (pp. 10727–10737).

Gidaris, S., & Komodakis, N. (2015). Object detection via a multi-region and semantic segmentation-aware CNN model. In ICCV (pp. 1134–1142). IEEE.

Girshick, R. (2015). Fast R-CNN. In: ICCV (pp. 1440–1448). IEEE.

Harzallah, H., Jurie, F., & Schmid, C. (2009). Combining efficient object localization and image classification. In ICCV (pp. 237–244). IEEE.

He, K., Gkioxari, G., Dollár, P., Girshick, R. (2017). Mask R-CNN. ICCV.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR (pp. 770–778). IEEE.

Heitz, G., & Koller, D. (2008). Learning spatial context: Using stuff to find things. In ECCV (pp. 30–43). Springer, Berlin.

Henderson, J. M., & Hollingworth, A. (1999). High-level scene perception. Annual Review of Psychology, 50(1), 243–271.CrossRef

Hinton, G. E., Sabour, S., & Frosst, N. (2018). Matrix capsules with EM routing. In ICLR.

Hollingworth, A. (1998). Does consistent scene context facilitate object perception? Journal of Experimental Psychology: General, 127(4), 398.CrossRef

Hu, H., Gu, J., Zhang, Z., Dai, J., & Wei, Y. (2018a). Relation networks for object detection. In CVPR (Vol. 2). IEEE.

Hu, J., Shen, L., & Sun, G. (2018b). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132–7141).

Kong, T., Yao, A., Chen, Y., & Sun, F. (2016). Hypernet: Towards accurate region proposal generation and joint object detection. In CVPR (pp. 845–853). IEEE.

Li, H., Guo, X., Dai, B., Ouyang, W., & Wang, X. (2018). Neural network encapsulation. In Proceedings of the European conference on computer vision (ECCV) (pp. 252–267).

Li, H., Liu, Y., Ouyang, W., & Wang, X. (2019). Zoom out-and-in network with map attention decision for region proposal and object detection. International Journal of Computer Vision, 127(3), 225–238.CrossRef

Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017a). Feature pyramid networks for object detection. In CVPR. IEEE.

Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017b). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980–2988).

Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2018). Focal loss for dense object detection. In TPAMI.

Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., et al. (2014). Microsoft coco: Common objects in context. In ECCV (pp. 740–755). Springer, Berlin.

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., et al. (2019). Deep learning for generic object detection: A survey. International Journal of Computer Vision, 128, 261–318.CrossRef

Liu, S., Huang, D., & Wang, A. (2018a). Receptive field block net for accurate and fast object detection. In ECCV. Springer, Berlin.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., et al. (2016). SSD: Single shot multibox detector. In ECCV (pp. 21–37). Springer, Berlin.

Liu, Y., Wang, R., Shan, S., & Chen, X. (2018b). Structure inference net: Object detection using scene-level context and instance-level relationships. In CVPR (pp. 6985–6994).

Modolo, D., Vezhnevets, A., & Ferrari, V. (2015). Context forest for object class detection (Vol. 1, p. 6). In BMVC.

Mordan, T., Thome, N., Henaff, G., & Cord, M. (2019). End-to-end learning of latent deformable part-based representations for object detection. International Journal of Computer Vision, 127(11–12), 1659–1679.CrossRef

Mottaghi, R., Chen, X., Liu, X., Cho, N. G., Lee, S. W., Fidler, S., et al. (2014). The role of context for object detection and semantic segmentation in the wild. In CVPR (pp. 891–898). IEEE.

Ouyang, W., Wang, K., Zhu, X., & Wang, X. (2017). Learning chained deep features and classifiers for cascade in object detection. In ICCV.

Ouyang, W., Zeng, X., & Wang, X. (2016). Learning mutual visibility relationship for pedestrian detection with a deep model. International Journal of Computer Vision, 120(1), 14–27.MathSciNetCrossRef

Palmer, T. E. (1975). The effects of contextual scenes on the identification of objects. Memory and Cognition, 3, 519–526.CrossRef

Qiao, S., Wang, H., Liu, C., Shen, W., & Yuille, A. (2019). Weight standardization. arXiv preprint arXiv:1903.10520.

Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., & Belongie, S. (2007). Objects in context. In ICCV (pp. 1–8). IEEE.

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In CVPR (pp. 779–788). IEEE.

Ren, J., Chen, X., Liu, J., Sun, W., Pang, J., Yan, Q., et al. (2017). Accurate single stage detector using recurrent rolling convolution. In CVPR.

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In NIPS (pp. 91–99).

Sabour, S., Frosst, N., & Hinton, G. E. (2017). Dynamic routing between capsules. In NIPS (pp. 3856–3866).

Shen, Z., Liu, Z., Li, J., Jiang, Y. G., Chen, Y., & Xue, X. (2017). Dsod: Learning deeply supervised object detectors from scratch. In CVPR (pp. 1919–1927). IEEE.

Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In ICLR.

Torralba, A., Murphy, K. P., Freeman, W. T., & Rubin, M. A., et al. (2003). Context-based vision system for place and object recognition. In ICCV (Vol. 3, pp. 273–280). IEEE.

Tu, Z., & Bai, X. (2010). Auto-context and its application to high-level vision tasks and 3d brain image segmentation. TPAMI, 32(10), 1744–1757.CrossRef

Vondrick, C., Khosla, A., Pirsiavash, H., Malisiewicz, T., & Torralba, A. (2016). Visualizing object detection features. International Journal of Computer Vision, 119(2), 145–158.MathSciNetCrossRef

Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., et al. (2018a). Understanding convolution for semantic segmentation. In: 2018 IEEE winter conference on applications of computer vision (WACV) (pp. 1451–1460). IEEE.

Wang, X., Girshick, R., Gupta, A., & He, K. (2018b). Non-local neural networks. In CVPR. IEEE.

Woo, S., Park, J., Lee, J. Y., & So Kweon, I. (2018). Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) (pp. 3–19).

Wu, Y., & He, K. (2018). Group normalization. In ECCV. Springer, Berlin.

Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1492–1500).

Yu, R. R., Chen, X. S., Morariu, V. I., Davis, L. S., & Redmond, W. (2010). The role of context selection in object detection. T-PAMI, 32(9), 1627–1645.CrossRef

Zagoruyko, S., Lerer, A., Lin, T. Y., Pinheiro, P. O., Gross, S., Chintala, S., et al. (2016). A multipath network for object detection. arXiv preprint arXiv:1604.02135.

Zeng, X., Ouyang, W., Yan, J., Li, H., Xiao, T., Wang, K., et al. (2017). Crafting gbd-net for object detection. T-PAMI, 40, 2109–2123.CrossRef

Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., et al. (2018). Context encoding for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7151–7160).

Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2881–2890).

Titel: Recursive Context Routing for Object Detection
verfasst von: Zhe Chen
Jing Zhang
Dacheng Tao
Publikationsdatum: 19.08.2020
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 1/2021
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-020-01370-7

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