Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image
Occlusion-aware instance-sensitive segmentation is a complex task generally split into region-based segmentations, by approximating instances as their bounding box. We address the showcase scenario of dense homogeneous layouts in which this approximation does not hold. In this scenario, outlining un...
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| Vydáno v: | International journal of computer vision Ročník 128; číslo 5; s. 1331 - 1359 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer US
01.05.2020
Springer Springer Nature B.V Springer Verlag |
| Edice: | Special Issue on Deep Learning for Robotic Vision |
| Témata: | |
| ISSN: | 0920-5691, 1573-1405 |
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| Abstract | Occlusion-aware instance-sensitive segmentation is a complex task generally split into region-based segmentations, by approximating instances as their bounding box. We address the showcase scenario of dense homogeneous layouts in which this approximation does not hold. In this scenario, outlining unoccluded instances by decoding a deep encoder becomes difficult, due to the translation invariance of convolutional layers and the lack of complexity in the decoder. We therefore propose a multicameral design composed of subtask-specific lightweight decoder and encoder–decoder units, coupled in cascade to encourage subtask-specific feature reuse and enforce a learning path within the decoding process. Furthermore, the state-of-the-art datasets for occlusion-aware instance segmentation contain real images with few instances and occlusions mostly due to objects occluding the background, unlike dense object layouts. We thus also introduce a synthetic dataset of dense homogeneous object layouts, namely Mikado, which extensibly contains more instances and inter-instance occlusions per image than these public datasets. Our extensive experiments on Mikado and public datasets show that ordinal multiscale units within the decoding process prove more effective than state-of-the-art design patterns for capturing position-sensitive representations. We also show that Mikado is plausible with respect to real-world problems, in the sense that it enables the learning of performance-enhancing representations transferable to real images, while drastically reducing the need of hand-made annotations for finetuning. The proposed dataset will be made publicly available. |
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| AbstractList | Occlusion-aware instance-sensitive segmentation is a complex task generally split into region-based segmentations, by approximating instances as their bounding box. We address the showcase scenario of dense homogeneous layouts in which this approximation does not hold. In this scenario, outlining unoccluded instances by decoding a deep encoder becomes difficult, due to the translation invariance of convolutional layers and the lack of complexity in the decoder. We therefore propose a multicameral design composed of subtask-specific lightweight decoder and encoder–decoder units, coupled in cascade to encourage subtask-specific feature reuse and enforce a learning path within the decoding process. Furthermore, the state-of-the-art datasets for occlusion-aware instance segmentation contain real images with few instances and occlusions mostly due to objects occluding the background, unlike dense object layouts. We thus also introduce a synthetic dataset of dense homogeneous object layouts, namely Mikado, which extensibly contains more instances and inter-instance occlusions per image than these public datasets. Our extensive experiments on Mikado and public datasets show that ordinal multiscale units within the decoding process prove more effective than state-of-the-art design patterns for capturing position-sensitive representations. We also show that Mikado is plausible with respect to real-world problems, in the sense that it enables the learning of performance-enhancing representations transferable to real images, while drastically reducing the need of hand-made annotations for finetuning. The proposed dataset will be made publicly available. |
| Audience | Academic |
| Author | Dellandréa, Emmanuel Chen, Liming Grard, Matthieu |
| Author_xml | – sequence: 1 givenname: Matthieu surname: Grard fullname: Grard, Matthieu email: m.grard@sileane.com organization: Siléane, Université de Lyon, CNRS, École Centrale de Lyon LIRIS UMR5205 – sequence: 2 givenname: Emmanuel surname: Dellandréa fullname: Dellandréa, Emmanuel organization: Université de Lyon, CNRS, École Centrale de Lyon LIRIS UMR5205 – sequence: 3 givenname: Liming surname: Chen fullname: Chen, Liming organization: Université de Lyon, CNRS, École Centrale de Lyon LIRIS UMR5205 |
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| CitedBy_id | crossref_primary_10_1016_j_neucom_2022_04_023 crossref_primary_10_1155_2022_8367387 |
| Cites_doi | 10.1109/CVPR.2016.28 10.1007/s11263-015-0816-y 10.1109/ICRA.2018.8460902 10.1109/76.678630 10.1109/CVPR.2017.187 10.1109/TPAMI.2016.2537320 10.1007/978-3-030-01424-7_58 10.1109/34.865184 10.1109/CVPR.2018.00214 10.1109/ICCV.2017.324 10.1109/CVPR.2016.352 10.1109/ICCV.2017.322 10.1007/978-3-319-46466-4_19 10.1109/CVPR.2011.5995517 10.1007/11744047_47 10.1007/978-3-030-01219-9_21 10.1109/CVPR.2016.207 10.1109/CVPR.2019.01063 10.1007/978-3-030-01249-6_35 10.1007/978-3-030-01234-2_49 10.1007/978-3-030-01219-9_24 10.1109/CVPR.2016.33 10.1109/ICCV.2001.937655 10.1109/CVPR.2016.433 10.1007/BF01679683 10.1109/CVPR.2017.34 10.1007/978-3-030-01246-5_6 10.1109/CVPR.2017.39 10.1109/TPAMI.2016.2644615 10.1109/CVPR.2017.243 10.1109/CVPR.2019.00512 10.1007/s11263-014-0733-5 10.1109/CVPR.2019.00313 10.1007/978-3-030-01219-9_14 10.1109/CVPR.2018.00047 10.1007/978-3-319-24574-4_28 10.1145/2647868.2654889 10.1109/CVPR.2018.00913 10.1007/s10994-009-5152-4 10.1109/TPAMI.2015.2505283 10.1007/s11263-011-0490-7 10.1109/CVPR.2017.622 10.1109/CVPR.2014.144 10.1109/CVPR.2017.70 10.1109/CVPR.2018.00940 10.1109/CVPR.2018.00132 10.1007/978-3-030-01231-1_35 10.5244/C.20.42 10.1007/978-3-319-46448-0_35 10.1007/978-3-319-46448-0_33 10.1109/CVPR.2019.00020 10.1109/WACV.2018.00163 10.1109/ICCV.2015.164 10.1109/CVPR.2019.00197 10.1109/CVPR.2017.774 10.1007/978-3-030-20876-9_43 10.1109/CVPR.2017.305 10.1109/CVPR.2016.343 10.1177/0278364913491297 10.1007/978-3-319-10602-1_48 10.1109/CVPR.2017.320 10.1109/ICCVW.2017.258 10.1109/WACV.2019.00146 10.1007/978-3-642-15561-1_39 10.1109/ICACI.2018.8377492 10.1109/ICCV.2017.296 10.1109/CVPR.2019.00626 |
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| Keywords | Instance boundary and occlusion detection Domain adaptation Fully convolutional encoder–decoder networks Synthetic data Fully convolutional encoder-decoder networks |
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| References | Wang, G., Wang, X., Li, F. W. B., & Liang, X. (2018a). DOOBNet: Deep object occlusion boundary detection from an image. In Asian conference on computer vision (ACCV) part VI (Vol. 11366, pp. 686–702). Lecture notes in computer science, Springer. Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Conference on computer vision and pattern recognition (CVPR) (pp. 2261–2269). IEEE Computer Society. Lin, T. Y., Goyal, P., Girshick, R. B., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In International conference on computer vision (ICCV) (pp. 2999–3007). IEEE Computer Society. Ren, X., Fowlkes, C. C., Malik, J. (2006). Figure/ground assignment in natural images. In European conference on computer vision (ECCV) part II (Vol. 3952, pp. 614–627). Lecture notes in computer science, Springer. Antoniou, A., Storkey, A. J., & Edwards, H. (2018). Augmenting image classifiers using data augmentation generative adversarial networks. In International conference on artificial neural networks and machine learning (ICANN) (Vol. 11141, pp. 594–603). Lecture notes in computer science, Springer. GeigerDLadendorfBYuilleALOcclusions and binocular stereoInternational Journal of Computer Vision (IJCV)199514321122610.1007/BF01679683 Luo, P., Wang, G., Lin, L., & Wang, X. (2017). Deep dual learning for semantic image segmentation. In International conference on computer vision (ICCV) (pp. 2737–2745). IEEE Computer Society. Ren, M., & Zemel, R. S. (2017). End-to-end instance segmentation with recurrent attention. In Conference on computer vision and pattern recognition (CVPR) (pp. 293–301). IEEE Computer Society. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Lecture notes in computer science (pp. 234–241). Springer. Liu, Y., Cheng, M. M., Hu, X., Wang, K., & Bai, X. (2017). Richer convolutional features for edge detection. In Conference on computer vision and pattern recognition (CVPR) (pp. 5872—5881). IEEE Computer Society. Cai, H., Zhu, L., & Han, S. (2019). ProxylessNAS: Direct neural architecture search on target task and hardware. In International conference on learning representations (ICLR). Hayder, Z., He, X., & Salzmann, M. (2017). Boundary-aware instance segmentation. In Conference on computer vision and pattern recognition (CVPR) (pp. 587–595). IEEE Computer Society. Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2019). AutoAugment: learning augmentation strategies from data. In Conference on computer vision and pattern recognition (CVPR) (pp. 113–123). Computer Vision Foundation/IEEE. Gan, Y., Xu, X., Sun, W., & Lin, L. (2018). Monocular depth estimation with affinity, vertical pooling, and label enhancement. In European conference on computer vision (ECCV) part III (Vol. 11207, pp. 232–247). Lecture notes in computer science, Springer. Yu, J., Yang, L., Xu, N., Yang, J., & Huang, T. (2019). Slimmable neural networks. In International conference on learning representations (ICLR). Maninis, K. K., Pont-Tuset, J., Arbeláez, P. A., & Gool, L. J. V. (2016). Convolutional oriented boundaries. In European conference on computer vision (ECCV) part I (Vol. 9905, pp. 580–596). Lecture notes in computer science, Springer. Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder–decoder with atrous separable convolution for semantic image segmentation. In European conference on computer vision (ECCV) part VII (Vol. 11211, pp. 833–851). Lecture notes in computer science, Springer. Xie, S., & Tu, Z. (2015). Holistically-nested edge detection. In International conference on computer vision (ICCV) (pp. 1395–1403). IEEE Computer Society. Kong, S., & Fowlkes, C. C. (2018). Recurrent pixel embedding for instance grouping. In Conference on computer vision and pattern recognition (CVPR) (pp. 9018–9028). IEEE Computer Society. Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., & Cottrell, G. W. (2018b). Understanding convolution for semantic segmentation. In Winter conference on applications of computer vision (WACV) (pp. 1451–1460). Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In International conference on multimedia (pp. 675–678). ACM, MM’14. Kirillov, A., Levinkov, E., Andres, B., Savchynskyy, B., & Rother, C. (2017). InstanceCut: From edges to instances with multicut. In Conference on computer vision and pattern recognition (CVPR) (pp. 7322–7331). IEEE Computer Society. Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems (NIPS) (pp. 3320–3328). Ben-David, S., Lu, T., Luu, T., Pál, D. (2010b). Impossibility theorems for domain adaptation. In International conference on artificial intelligence and statistics (AISTATS), JMLR.org, JMLR proceedings (Vol. 9, pp. 129–136). Zhu, Y., Tian, Y., Metaxas, D. N., Dollár, P. (2017). Semantic amodal segmentation. In Conference on computer vision and pattern recognition (CVPR) (pp. 3001–3009). IEEE Computer Society. Yu, F., & Koltun, V. (2016). Multi-scale context aggregation by dilated convolutions. In International conference on learning representations (ICLR). Lee, W., Na, J., & Kim, G. (2019). Multi-task self-supervised object detection via recycling of bounding box annotations. In Conference on computer vision and pattern recognition (CVPR) (pp. 4984–4993). Computer Vision Foundation/IEEE. Novotný, D., Albanie, S., Larlus, D., & Vedaldi, A. (2018). Semi-convolutional operators for instance segmentation. In European conference on computer vision (ECCV) part I (Vol. 11205, pp. 89–105). Lecture notes in computer science, Springer. Yu, Z., Liu, W., Zou, Y., Feng, C., Ramalingam, S., Kumar, B. V. K. V., & Kautz, J. (2018). Simultaneous edge alignment and learning. In European conference on computer vision (ECCV) part III (Vol. 11207, pp. 400–417). Lecture notes in computer science, Springer. Eigen, D., Puhrsch, C., & Fergus, R. (2014). Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems (NIPS) (pp. 2366–2374). Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In International conference on artificial intelligence and statistics (AISTATS), JMLR.org, JMLR proceedings (Vol. 9, pp. 249–256) Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International conference on learning representations (ICLR), IEEE Computer Society. Romera-Paredes, B., & Torr, P. H. S. (2016). Recurrent instance segmentation. In European conference on computer vision (ECCV) part VI (Vol. 9910, pp. 312–329). Lecture notes in computer science, Springer. Wang, P., & Yuille, A. L. (2016). DOC: Deep occlusion estimation from a single image. In European conference on computer vision (ECCV) part I (Vol. 9905, pp. 545–561). Lecture notes in computer science, Springer. Dai, J., He, K., & Sun, J. (2016). Instance-aware semantic segmentation via multi-task network cascades. In Conference on computer vision and pattern recognition (CVPR) (pp. 3150–3158). IEEE Computer Society. ZitnickCLKanadeTA cooperative algorithm for stereo matching and occlusion detectionIEEE Transactions on Pattern Analysis Machine Intelligence (TPAMI)200022767568410.1109/34.865184 Batra, A., Singh, S., Pang, G., Basu, S., Jawahar, C., & Paluri, M. (2019). Improved road connectivity by joint learning of orientation and segmentation. In Conference on computer vision and pattern recognition (CVPR) (pp. 10385–10393). Computer Vision Foundation/IEEE. GeigerALenzPStillerCUrtasunRVision meets robotics: The KITTI datasetInternational Journal of Robotics Research (IJRR)201332111231123710.1177/0278364913491297 Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In International conference on computer vision (ICCV) (pp. 416–423). IEEE Computer Society. Ayvaci, A., Raptis, M., & Soatto, S. (2010). Occlusion detection and motion estimation with convex optimization. In Advances in neural information processing systems (NIPS) (pp. 100–108). Do, T. T., Nguyen, A., & Reid, I. D. (2018). AffordanceNet: An end-to-end deep learning approach for object affordance detection. In International conference on robotics and automation (ICRA) (pp. 1–5). IEEE. Yang, J., Price, B. L., Cohen, S., Lee, H., & Yang, M. H. (2016). Object contour detection with a fully convolutional encoder–decoder network. In Conference on computer vision and pattern recognition (CVPR) BadrinarayananVKendallACipollaRSegNet: A deep convolutional encoder–decoder architecture for image segmentationIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)201739122481249510.1109/TPAMI.2016.2644615 Blender Online Community. (2016). Blender—a 3D modelling and rendering package. Blender Foundation, Blender Institute, Amsterdam, http://www.blender.org. Follmann, P., König, R., Härtinger, P., Klostermann, M., & Böttger, T. (2019). Learning to see the invisible: End-to-end trainable amodal instance segmentation. In Winter conference on applications of computer vision, (WACV) (pp. 1328–1336). IEEE. Misra, I., Shrivastava, A., Gupta, A., & Hebert, M. (2016). Cross-stitch networks for multi-task learning. In Conference on computer vision and pattern recognition (CVPR) (pp. 3994–4003). IEEE Computer Society. Follmann, P., Böttger, T., Härtinger, P., König, R., & Ulrich, M. (2018). MVTec D2S: Densely segmented supermarket dataset. In European conference on computer vision (ECCV) part X (Vol. 11214, pp. 581–597). Lecture notes in computer science, Springer. 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| References_xml | – reference: EveringhamMEslamiSMGoolLWilliamsCKWinnJZissermanAThe pascal visual object classes challenge: A retrospectiveInternational Journal of Computer Vision (IJCV)201511119813610.1007/s11263-014-0733-5 – reference: Qi, L., Jiang, L., Liu, S., Shen, X., & Jia, J. (2019). Amodal instance segmentation with KINS dataset. In Conference on computer vision and pattern recognition (CVPR) (pp. 3014–3023). Computer Vision Foundation/IEEE. – reference: Stein, A., & Hebert, M. (2006). Local detection of occlusion boundaries in video. In British machine vision conference (BMVC). – reference: He, X., & Yuille, A. (2010). Occlusion boundary detection using pseudo-depth. In European conference on computer vision (ECCV) part IV (Vol. 6314, pp. 539–552). Lecture notes in computer science, Springer. – reference: Antoniou, A., Storkey, A. J., & Edwards, H. (2018). Augmenting image classifiers using data augmentation generative adversarial networks. In International conference on artificial neural networks and machine learning (ICANN) (Vol. 11141, pp. 594–603). Lecture notes in computer science, Springer. – reference: Blender Online Community. (2016). Blender—a 3D modelling and rendering package. Blender Foundation, Blender Institute, Amsterdam, http://www.blender.org. – reference: Dai, J., He, K., & Sun, J. (2016). Instance-aware semantic segmentation via multi-task network cascades. In Conference on computer vision and pattern recognition (CVPR) (pp. 3150–3158). IEEE Computer Society. – reference: Caesar, H., Uijlings, J. R. R., Ferrari, V. (2018). COCO-Stuff: Thing and stuff classes in context. In Conference on computer vision and pattern recognition (CVPR) (pp. 1209–1218). IEEE Computer Society. – reference: Pont-TusetJArbelaezPBarronJTMarquésFMalikJMultiscale combinatorial grouping for image segmentation and object proposal generationIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)201739112814010.1109/TPAMI.2016.2537320 – reference: Lee, W., Na, J., & Kim, G. (2019). Multi-task self-supervised object detection via recycling of bounding box annotations. In Conference on computer vision and pattern recognition (CVPR) (pp. 4984–4993). Computer Vision Foundation/IEEE. – reference: LiuFShenCLinGReidIDLearning depth from single monocular images using deep convolutional neural fieldsIEEE Transactions on Pattern Analysis Machine Intelligence (TPAMI)201638102024203910.1109/TPAMI.2015.2505283 – reference: Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Lecture notes in computer science (pp. 234–241). Springer. – reference: Bai, M., Urtasun, R. (2017). Deep watershed transform for instance segmentation. In Conference on computer vision and pattern recognition (CVPR) (pp. 2858–2866). IEEE Computer Society. – reference: Zhu, Y., Tian, Y., Metaxas, D. N., Dollár, P. (2017). Semantic amodal segmentation. In Conference on computer vision and pattern recognition (CVPR) (pp. 3001–3009). IEEE Computer Society. – reference: Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In International conference on artificial intelligence and statistics (AISTATS), JMLR.org, JMLR proceedings (Vol. 9, pp. 249–256) – reference: Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., & Cottrell, G. W. (2018b). Understanding convolution for semantic segmentation. In Winter conference on applications of computer vision (WACV) (pp. 1451–1460). – reference: Ren, X., Fowlkes, C. C., Malik, J. (2006). Figure/ground assignment in natural images. In European conference on computer vision (ECCV) part II (Vol. 3952, pp. 614–627). Lecture notes in computer science, Springer. – reference: Brégier, R., Devernay, F., Leyrit, L., & Crowley, J. L. (2017). Symmetry aware evaluation of 3d object detection and pose estimation in scenes of many parts in bulk. In International conference on computer vision workshops (ICCVW) (pp. 2209–2218). IEEE Computer Society. – reference: Misra, I., Shrivastava, A., Gupta, A., & Hebert, M. (2016). Cross-stitch networks for multi-task learning. In Conference on computer vision and pattern recognition (CVPR) (pp. 3994–4003). IEEE Computer Society. – reference: Batra, A., Singh, S., Pang, G., Basu, S., Jawahar, C., & Paluri, M. (2019). Improved road connectivity by joint learning of orientation and segmentation. In Conference on computer vision and pattern recognition (CVPR) (pp. 10385–10393). Computer Vision Foundation/IEEE. – reference: Yu, Z., Liu, W., Zou, Y., Feng, C., Ramalingam, S., Kumar, B. V. K. V., & Kautz, J. (2018). Simultaneous edge alignment and learning. In European conference on computer vision (ECCV) part III (Vol. 11207, pp. 400–417). Lecture notes in computer science, Springer. – reference: Follmann, P., Böttger, T., Härtinger, P., König, R., & Ulrich, M. (2018). MVTec D2S: Densely segmented supermarket dataset. In European conference on computer vision (ECCV) part X (Vol. 11214, pp. 581–597). Lecture notes in computer science, Springer. – reference: GeigerALenzPStillerCUrtasunRVision meets robotics: The KITTI datasetInternational Journal of Robotics Research (IJRR)201332111231123710.1177/0278364913491297 – reference: Sun, D., Liu, C., & Pfister, H. (2014). Local layering for joint motion estimation and occlusion detection. In Conference on computer vision and pattern recognition (CVPR) (pp. 1098–1105). IEEE Computer Society. – reference: Ros, G., Sellart, L., Materzynska, J., Vázquez, D., & López, A. M. (2016). The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In Conference on computer vision and pattern recognition (CVPR) (pp. 3234–3243). IEEE Computer Society. – reference: Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder–decoder with atrous separable convolution for semantic image segmentation. In European conference on computer vision (ECCV) part VII (Vol. 11211, pp. 833–851). Lecture notes in computer science, Springer. – reference: Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Conference on computer vision and pattern recognition (CVPR) (pp. 2261–2269). IEEE Computer Society. – reference: Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. In European conference on computer vision (ECCV) Part V (Vol. 8693, pp. 740–755). Lecture notes in computer science, Springer. – reference: Guan, S., Khan, A. A., Sikdar, S., Chitnis, P. V. (2018). Fully dense UNet for 2D sparse photoacoustic tomography artifact removal. Journal of Biomedical and Health Informatics. – reference: Yang, J., Price, B. L., Cohen, S., Lee, H., & Yang, M. H. (2016). Object contour detection with a fully convolutional encoder–decoder network. In Conference on computer vision and pattern recognition (CVPR) – reference: AyvaciARaptisMSoattoSSparse occlusion detection with optical flowInternational Journal of Computer Vision (IJCV)2012973322338290420410.1007/s11263-011-0490-7 – reference: Liu, G., Si, J., Hu, Y., & Li, S. (2018a). Photographic image synthesis with improved U-net. In International conference on advanced computational intelligence (ICACI) (pp. 402–407). IEEE. – reference: Xie, S., & Tu, Z. (2015). Holistically-nested edge detection. In International conference on computer vision (ICCV) (pp. 1395–1403). IEEE Computer Society. – reference: Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018c). Path aggregation network for instance segmentation. In Conference on computer vision and pattern recognition (CVPR) (pp. 8759–8768). IEEE Computer Society. – reference: Eigen, D., Puhrsch, C., & Fergus, R. (2014). Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems (NIPS) (pp. 2366–2374). – reference: Kendall, A., Gal, Y., & Cipolla, R. (2018). Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In Conference on computer vision and pattern recognition (CVPR) (pp. 7482–7491). IEEE Computer Society. – reference: Wang, P., & Yuille, A. L. (2016). DOC: Deep occlusion estimation from a single image. In European conference on computer vision (ECCV) part I (Vol. 9905, pp. 545–561). Lecture notes in computer science, Springer. – reference: McCormac, J., Handa, A., Leutenegger, S., & Davison, A. J. (2017). SceneNet RGB-D: Can 5M synthetic images beat generic imagenet pre-training on indoor segmentation? In International conference on computer vision (ICCV) (pp. 2697–2706). IEEE Computer Society. – reference: Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International conference on learning representations (ICLR), IEEE Computer Society. – reference: Ayvaci, A., Raptis, M., & Soatto, S. (2010). Occlusion detection and motion estimation with convex optimization. In Advances in neural information processing systems (NIPS) (pp. 100–108). – reference: GeigerDLadendorfBYuilleALOcclusions and binocular stereoInternational Journal of Computer Vision (IJCV)199514321122610.1007/BF01679683 – reference: Kirillov, A., Wu, Y., He, K., & Girshick, R. B. (2019). PointRend: Image segmentation as rendering. CoRR, arXiv:1912.08193, http://arxiv.org/abs/1912.08193 – reference: Kirillov, A., Levinkov, E., Andres, B., Savchynskyy, B., & Rother, C. (2017). InstanceCut: From edges to instances with multicut. In Conference on computer vision and pattern recognition (CVPR) (pp. 7322–7331). IEEE Computer Society. – reference: BadrinarayananVKendallACipollaRSegNet: A deep convolutional encoder–decoder architecture for image segmentationIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)201739122481249510.1109/TPAMI.2016.2644615 – reference: Ben-DavidSBlitzerJCrammerKKuleszaAPereiraFVaughanJWA theory of learning from different domainsMachine Learning2010791–2151175310815010.1007/s10994-009-5152-4 – reference: Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In International conference on learning representations (ICLR). – reference: Luo, P., Wang, G., Lin, L., & Wang, X. (2017). Deep dual learning for semantic image segmentation. In International conference on computer vision (ICCV) (pp. 2737–2745). IEEE Computer Society. – reference: Fu, H., Gong, M., Wang, C., Batmanghelich, K., & Tao, D. (2018). Deep ordinal regression network for monocular depth estimation. In Conference on computer vision and pattern recognition (CVPR) (pp. 2002–2011). IEEE Computer Society. – reference: Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems (NIPS) (pp. 3320–3328). – reference: Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Conference on computer vision and pattern recognition (CVPR) (pp. 1874–1883). IEEE Computer Society. – reference: Kong, S., & Fowlkes, C. C. (2018). Recurrent pixel embedding for instance grouping. In Conference on computer vision and pattern recognition (CVPR) (pp. 9018–9028). IEEE Computer Society. – reference: Yu, J., Yang, L., Xu, N., Yang, J., & Huang, T. (2019). Slimmable neural networks. In International conference on learning representations (ICLR). – reference: Do, T. T., Nguyen, A., & Reid, I. D. (2018). AffordanceNet: An end-to-end deep learning approach for object affordance detection. In International conference on robotics and automation (ICRA) (pp. 1–5). IEEE. – reference: Hayder, Z., He, X., & Salzmann, M. (2017). Boundary-aware instance segmentation. In Conference on computer vision and pattern recognition (CVPR) (pp. 587–595). IEEE Computer Society. – reference: Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In International conference on multimedia (pp. 675–678). ACM, MM’14. – reference: Li, G., Xie, Y., Lin, L., & Yu, Y. (2017). Instance-level salient object segmentation. In Conference on computer vision and pattern recognition (CVPR) (pp. 247–256). IEEE Computer Society. – reference: Yu, F., & Koltun, V. (2016). Multi-scale context aggregation by dilated convolutions. In International conference on learning representations (ICLR). – reference: Liu, S., Johns, E., & Davison, A. J. (2019). End-to-end multi-task learning with attention. In Conference on computer vision and pattern recognition (CVPR) (pp. 1871–1880). Computer Vision Foundation/IEEE. – reference: Romera-Paredes, B., & Torr, P. H. S. (2016). Recurrent instance segmentation. In European conference on computer vision (ECCV) part VI (Vol. 9910, pp. 312–329). Lecture notes in computer science, Springer. – reference: Liu, Y., Cheng, M. M., Hu, X., Wang, K., & Bai, X. (2017). Richer convolutional features for edge detection. In Conference on computer vision and pattern recognition (CVPR) (pp. 5872—5881). IEEE Computer Society. – reference: Deng, R., Shen, C., Liu, S., Wang, H., & Liu, X. (2018). Learning to predict crisp boundaries. In European conference on computer vision (ECCV) part VI (Vol. 11210, pp. 570–586). Lecture notes in computer science, Springer. – reference: Liu, R., Lehman, J., Molino, P., Such, F. P., Frank, E., Sergeev, A., & Yosinski, J. (2018b). An intriguing failing of convolutional neural networks and the coordconv solution. In Advances in neural information processing systems (NeurIPS) (pp. 9628–9639). – reference: ZitnickCLKanadeTA cooperative algorithm for stereo matching and occlusion detectionIEEE Transactions on Pattern Analysis Machine Intelligence (TPAMI)200022767568410.1109/34.865184 – reference: Follmann, P., König, R., Härtinger, P., Klostermann, M., & Böttger, T. (2019). Learning to see the invisible: End-to-end trainable amodal instance segmentation. In Winter conference on applications of computer vision, (WACV) (pp. 1328–1336). IEEE. – reference: Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2019). AutoAugment: learning augmentation strategies from data. In Conference on computer vision and pattern recognition (CVPR) (pp. 113–123). Computer Vision Foundation/IEEE. – reference: Wang, G., Wang, X., Li, F. W. B., & Liang, X. (2018a). DOOBNet: Deep object occlusion boundary detection from an image. In Asian conference on computer vision (ACCV) part VI (Vol. 11366, pp. 686–702). Lecture notes in computer science, Springer. – reference: Humayun, A., Mac Aodha, O., Brostow, G. J. (2011). Learning to find occlusion regions. In Conference on computer vision and pattern recognition (CVPR) (pp. 2161–2168). IEEE Computer Society. – reference: Williams, O., Isard, M., & MacCormick., J. (2011). Estimating disparity and occlusions in stereo video sequences. In Conference on computer vision and pattern recognition (CVPR) (pp. 250–257). IEEE Computer Society. – reference: Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016). Virtual worlds as proxy for multi-object tracking analysis. In Conference on computer vision and pattern recognition (CVPR), IEEE Computer Society. – reference: Wang, Y., Zhao, X., & Huang, K. (2017). Deep crisp boundaries. In Conference on computer vision and pattern recognition (CVPR) (pp. 1724–1732). IEEE Computer Society. – reference: RussakovskyODengJSuHKrauseJSatheeshSMaSHuangZKarpathyAKhoslaABernsteinMBergACFei-FeiLImageNet large scale visual recognition challengeInternational Journal of Computer Vision (IJCV)20151153211252342248210.1007/s11263-015-0816-y – reference: Cai, H., Zhu, L., & Han, S. (2019). ProxylessNAS: Direct neural architecture search on target task and hardware. In International conference on learning representations (ICLR). – reference: Dong, X., Yan, Y., Ouyang, W., Yang, Y. (2018). Style aggregated network for facial landmark detection. In Conference on computer vision and pattern recognition (CVPR) (pp. 379–388). IEEE Computer Society. – reference: Fan, R., Cheng, M. M., Hou, Q., Mu, T. J., Wang, J., & Hu, S. M. (2019). S4Net: Single stage salient-instance segmentation. In Conference on computer vision and pattern recognition (CVPR) (pp. 6103–6112). Computer Vision Foundation/IEEE. – reference: He, K., Gkioxari, G., Dollár, P., & Girshick, R. B. (2017). Mask R-CNN. In International conference on computer vision (ICCV) (pp. 2980–2988). IEEE Computer Society. – reference: Maninis, K. K., Pont-Tuset, J., Arbeláez, P. A., & Gool, L. J. V. (2016). Convolutional oriented boundaries. In European conference on computer vision (ECCV) part I (Vol. 9905, pp. 580–596). Lecture notes in computer science, Springer. – reference: Lin, T. Y., Goyal, P., Girshick, R. B., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In International conference on computer vision (ICCV) (pp. 2999–3007). IEEE Computer Society. – reference: Novotný, D., Albanie, S., Larlus, D., & Vedaldi, A. (2018). Semi-convolutional operators for instance segmentation. In European conference on computer vision (ECCV) part I (Vol. 11205, pp. 89–105). Lecture notes in computer science, Springer. – reference: Zhang, L., Li, X., Arnab, A., Yang, K., Tong, Y., & Torr, P. H. (2019). Dual graph convolutional network for semantic segmentation. In British machine vision conference (BMVC). – reference: Gan, Y., Xu, X., Sun, W., & Lin, L. (2018). Monocular depth estimation with affinity, vertical pooling, and label enhancement. In European conference on computer vision (ECCV) part III (Vol. 11207, pp. 232–247). Lecture notes in computer science, Springer. – reference: GrammalidisNStrintzisMGDisparity and occlusion estimation in multiocular systems and their coding for the communication of multiview image sequencesTransactions on Circuits and Systems for Video Technology (TCSVT)19988332834410.1109/76.678630 – reference: Tang, Z., Peng, X., Geng, S., Wu, L., Zhang, S., & Metaxas, D. N. (2018). Quantized densely connected U-Nets for efficient landmark localization. In European conference on computer vision (ECCV) part III (Vol. 11207, pp. 348–364). Lecture notes in computer science, Springer. – reference: Ren, M., & Zemel, R. S. (2017). End-to-end instance segmentation with recurrent attention. In Conference on computer vision and pattern recognition (CVPR) (pp. 293–301). IEEE Computer Society. – reference: Fu, H., Wang, C., Tao, D., & Black, M. J. (2016). Occlusion boundary detection via deep exploration of context. In Conference on computer vision and pattern recognition (CVPR) (pp. 241–250). IEEE Computer Society. – reference: Ben-David, S., Lu, T., Luu, T., Pál, D. (2010b). Impossibility theorems for domain adaptation. In International conference on artificial intelligence and statistics (AISTATS), JMLR.org, JMLR proceedings (Vol. 9, pp. 129–136). – reference: Grard, M., Brégier, R., Sella, F., Dellandréa, E., & Chen, L. (2018). Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network. In 2017 international workshop on human-friendly robotics (Vol. 7, pp. 207–221). Springer proceedings in advanced robotics, Springer. – reference: Li, B., Shen, C., Dai, Y., van den Hengel, A., & He, M. (2015). Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs. In Conference on computer vision and pattern recognition (CVPR) (pp. 1119–1127). IEEE Computer Society. – reference: Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In International conference on computer vision (ICCV) (pp. 416–423). IEEE Computer Society. – ident: 1323_CR81 doi: 10.1109/CVPR.2016.28 – volume: 115 start-page: 211 issue: 3 year: 2015 ident: 1323_CR69 publication-title: International Journal of Computer Vision (IJCV) doi: 10.1007/s11263-015-0816-y – ident: 1323_CR17 doi: 10.1109/ICRA.2018.8460902 – volume: 8 start-page: 328 issue: 3 year: 1998 ident: 1323_CR31 publication-title: Transactions on Circuits and Systems for Video Technology (TCSVT) doi: 10.1109/76.678630 – ident: 1323_CR78 doi: 10.1109/CVPR.2017.187 – volume: 39 start-page: 128 issue: 1 year: 2017 ident: 1323_CR62 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) doi: 10.1109/TPAMI.2016.2537320 – ident: 1323_CR1 doi: 10.1007/978-3-030-01424-7_58 – ident: 1323_CR79 – volume: 22 start-page: 675 issue: 7 year: 2000 ident: 1323_CR88 publication-title: IEEE Transactions on Pattern Analysis Machine Intelligence (TPAMI) doi: 10.1109/34.865184 – ident: 1323_CR24 doi: 10.1109/CVPR.2018.00214 – ident: 1323_CR46 – ident: 1323_CR48 doi: 10.1109/ICCV.2017.324 – ident: 1323_CR68 doi: 10.1109/CVPR.2016.352 – ident: 1323_CR8 – ident: 1323_CR35 doi: 10.1109/ICCV.2017.322 – ident: 1323_CR66 doi: 10.1007/978-3-319-46466-4_19 – ident: 1323_CR38 doi: 10.1109/CVPR.2011.5995517 – ident: 1323_CR65 doi: 10.1007/11744047_47 – ident: 1323_CR74 doi: 10.1007/978-3-030-01219-9_21 – ident: 1323_CR71 – ident: 1323_CR52 – ident: 1323_CR33 – ident: 1323_CR70 doi: 10.1109/CVPR.2016.207 – ident: 1323_CR6 doi: 10.1109/CVPR.2019.01063 – ident: 1323_CR22 doi: 10.1007/978-3-030-01249-6_35 – ident: 1323_CR13 doi: 10.1007/978-3-030-01234-2_49 – ident: 1323_CR85 doi: 10.1007/978-3-030-01219-9_24 – ident: 1323_CR25 doi: 10.1109/CVPR.2016.33 – ident: 1323_CR58 doi: 10.1109/ICCV.2001.937655 – ident: 1323_CR82 – ident: 1323_CR60 doi: 10.1109/CVPR.2016.433 – ident: 1323_CR41 – volume: 14 start-page: 211 issue: 3 year: 1995 ident: 1323_CR29 publication-title: International Journal of Computer Vision (IJCV) doi: 10.1007/BF01679683 – ident: 1323_CR47 doi: 10.1109/CVPR.2017.34 – ident: 1323_CR61 doi: 10.1007/978-3-030-01246-5_6 – ident: 1323_CR64 doi: 10.1109/CVPR.2017.39 – ident: 1323_CR86 – volume: 39 start-page: 2481 issue: 12 year: 2017 ident: 1323_CR4 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) doi: 10.1109/TPAMI.2016.2644615 – ident: 1323_CR37 doi: 10.1109/CVPR.2017.243 – ident: 1323_CR45 doi: 10.1109/CVPR.2019.00512 – ident: 1323_CR59 – volume: 111 start-page: 98 issue: 1 year: 2015 ident: 1323_CR20 publication-title: International Journal of Computer Vision (IJCV) doi: 10.1007/s11263-014-0733-5 – ident: 1323_CR63 doi: 10.1109/CVPR.2019.00313 – ident: 1323_CR27 doi: 10.1007/978-3-030-01219-9_14 – ident: 1323_CR30 – ident: 1323_CR18 doi: 10.1109/CVPR.2018.00047 – ident: 1323_CR67 doi: 10.1007/978-3-319-24574-4_28 – ident: 1323_CR39 doi: 10.1145/2647868.2654889 – ident: 1323_CR54 doi: 10.1109/CVPR.2018.00913 – ident: 1323_CR2 – volume: 79 start-page: 151 issue: 1–2 year: 2010 ident: 1323_CR7 publication-title: Machine Learning doi: 10.1007/s10994-009-5152-4 – ident: 1323_CR40 – volume: 38 start-page: 2024 issue: 10 year: 2016 ident: 1323_CR50 publication-title: IEEE Transactions on Pattern Analysis Machine Intelligence (TPAMI) doi: 10.1109/TPAMI.2015.2505283 – ident: 1323_CR9 – volume: 97 start-page: 322 issue: 3 year: 2012 ident: 1323_CR3 publication-title: International Journal of Computer Vision (IJCV) doi: 10.1007/s11263-011-0490-7 – ident: 1323_CR83 – ident: 1323_CR55 doi: 10.1109/CVPR.2017.622 – ident: 1323_CR73 doi: 10.1109/CVPR.2014.144 – ident: 1323_CR34 doi: 10.1109/CVPR.2017.70 – ident: 1323_CR12 – ident: 1323_CR26 – ident: 1323_CR44 doi: 10.1109/CVPR.2018.00940 – ident: 1323_CR11 doi: 10.1109/CVPR.2018.00132 – ident: 1323_CR16 doi: 10.1007/978-3-030-01231-1_35 – ident: 1323_CR72 doi: 10.5244/C.20.42 – ident: 1323_CR43 – ident: 1323_CR57 doi: 10.1007/978-3-319-46448-0_35 – ident: 1323_CR76 doi: 10.1007/978-3-319-46448-0_33 – ident: 1323_CR14 doi: 10.1109/CVPR.2019.00020 – ident: 1323_CR77 doi: 10.1109/WACV.2018.00163 – ident: 1323_CR80 doi: 10.1109/ICCV.2015.164 – ident: 1323_CR53 doi: 10.1109/CVPR.2019.00197 – ident: 1323_CR42 doi: 10.1109/CVPR.2017.774 – ident: 1323_CR75 doi: 10.1007/978-3-030-20876-9_43 – ident: 1323_CR84 – ident: 1323_CR5 doi: 10.1109/CVPR.2017.305 – ident: 1323_CR15 doi: 10.1109/CVPR.2016.343 – volume: 32 start-page: 1231 issue: 11 year: 2013 ident: 1323_CR28 publication-title: International Journal of Robotics Research (IJRR) doi: 10.1177/0278364913491297 – ident: 1323_CR49 doi: 10.1007/978-3-319-10602-1_48 – ident: 1323_CR87 doi: 10.1109/CVPR.2017.320 – ident: 1323_CR10 doi: 10.1109/ICCVW.2017.258 – ident: 1323_CR19 – ident: 1323_CR23 doi: 10.1109/WACV.2019.00146 – ident: 1323_CR36 doi: 10.1007/978-3-642-15561-1_39 – ident: 1323_CR51 doi: 10.1109/ICACI.2018.8377492 – ident: 1323_CR56 doi: 10.1109/ICCV.2017.296 – ident: 1323_CR21 doi: 10.1109/CVPR.2019.00626 – ident: 1323_CR32 |
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