🎰 The KITTI Vision Benchmark Suite

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The last leaderboards right before this change can be found here: Object J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale.


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finetuned the JPP-Net[1], SS-NAN[2], SSL[3], DenseNet[4], RefineNet[5] on LIP training set. Then we combined the five models with different fusion strategies.


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finetuned the JPP-Net[1], SS-NAN[2], SSL[3], DenseNet[4], RefineNet[5] on LIP training set. Then we combined the five models with different fusion strategies.


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Then we combined the five models with different fusion strategies. Abbreviation Contributors Description 4paradigm.{/INSERTKEYS}{/PARAGRAPH} Single-Person Human Parsing Track Metrics We use four metrics from common semantic segmentation and scene parsing evaluations that are variations on pixel accuracy and region intersection over union IU. Abbreviation Contributors Description WuTao. Then we combined the two models with different fusion strategies. If you would like to submit your results, please register, login, and follow the instructions on our submission page. Abbreviation Contributors Description hanqiuyuan. We proposed a novel CE2P[1] network, which consists of three key modules to learn for parsing in an end-to-end manner: 1 high resolution embedding module; 2 global context embedding module; 3 edge perceiving module. SS-NAN can be integrated into any advanced neural networks to help aggregate features regarding the importance at different positions and scales and incorporate rich high-level knowledge regarding human joint structures from a global perspective, which in turn improve the parsing results. Contributors Description test test Contributors Description 3rd0. We use four metrics from common semantic segmentation and scene parsing evaluations that are variations on pixel accuracy and region intersection over union IU. No extra datasets were used. However, without careful design of network architectures, deep models such as ResNet cannot fully utilize the atrous convolution structure proposed in [1] to leverage the advantage of multi-scale features. It has two core contributions. Finally, I concat the Unit1 of Block2 with the resized and concated feature maps. Abbreviation Contributors Description.. We offer a Dataset that contains more than pictures, including images for training set, images for validation set and images for test set. Abbreviation Contributors Description. The best results were obtained by combining them. Contributors Description.. The view groundtruth is automatically generated by analyzing the parsing groundtruth with human knowledge. Our best result is produced by an ensemble of three models. Contributors Description Motoki Kimura. The view classifier predicts whether the human is in frontal or back view. Contributors Description 1 1. It has been demonstrated that multi-scale features are useful to improve the performance of semantic segmentation. Abbreviation Contributors Description 1. In order to further improve the feature discriminative capacity, a self-supervised joint loss is adopted as an auxiliary learning strategy, which imposes human joint structures into parsing results without resorting to extra supervision. CVPR Contributors Description Haoshu Fang, Yuwing Tai, Cewu Lu It has been demonstrated that multi-scale features are useful to improve the performance of semantic segmentation. AAAI Contributors Description Zllrunning First try! Moreover, to further boost the overall performance of SS-NAN for human parsing, we also leverage a robust multi-view strategy with different state-of-the-art backbone models. SS-NAN adaptively learns to aggregate the multi-scale features at each pixel "address". {PARAGRAPH}{INSERTKEYS}We offer a benchmark suite together with an evaluation server, such that authors can upload their results and get a ranking. The classification results serve as the context of the parsing and boost the performances. Contributors Description. Note: We only display the highest submission of each person. Abbreviation Contributors Description Zllrunning. In this work, we propose 'WhiskNet', which utilizes building blocks of ResNet, to extract and incorporate very deep multi-scale features into a single network model. The metrics are reported by FCN. Details background hat hair glove sunglasses upper-clothes dress coat socks pants Abbreviation Contributors Description Anonymous. Meanwhile, I remove the last layer of densenet and the maxpool layer to get the feature map. Moreover, 'WhiskNet' adds an extra 'Multi-atrous-convolution' for each scale which achieves excellent performance when merging multi-scale features. Abbreviation Contributors Description test Abbreviation Contributors Description Motoki Kimura. We have proposed a view-specific contextual human parsing method. Overview We offer a benchmark suite together with an evaluation server, such that authors can upload their results and get a ranking.