Please refer to https://arxiv.org/abs/1808.03833 for details. OCR is an extension of object context networks https://arxiv.org/pdf/1809.00916.pdf, The proposed network architecture, combined with spatial information and multi scale context information, and repair the boundaries and details of the segmented object through channel attention modules. Importantly, since it is expensive to obtain pixel-wise annotations, we exploit a new training method for combining the coarsely and finely labeled data. Specifically, features at each level are enhanced by higher-level features with stronger semantics and lower-level features with more details, and gates are used to control the pass of useful information which significantly reducing noise propagation during fusion. In this work, we study Neural Architecture Search for semantic image segmentation, an important computer vision task that assigns a semantic label to every pixel in an image. (This is a revision of a previous submission in which we didn't use the correct basis functions; the method name changed from 'LLR-4x' to 'LRR-4x'). Bowen Cheng, Liang-Chieh Chen, Yunchao Wei, Yukun Zhu, Zilong Huang, Jinjun Xiong, Thomas Huang, Wen-Mei Hwu, Honghui Shi. Currently, many state of the art models are based on the Mask R-CNN framework which, while very powerful, outputs masks at low resolutions which could result in imprecise boundaries. MaskRCNN segmentation baseline for Bosh autodrive challenge , Global Concatenating Feature Enhancement for Instance Segmentation, Hang Yang, Xiaozhe Xin, Wenwen Yang, Bin Li, Davy Neven, Bert De Brabandere, Marc Proesmans and Luc Van Gool, 2019 IEEE Intelligent Vehicles Symposium (IV). Social game design operates within the physical and mental constraints of the human animal, so it pays to understand these constraints and build them into our designs. We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Mask R-CNN based on FPN enhancement and Mask Rescore, etc. AKA -> How to generate YOLO anchors? Our proposed methods achieve state-of-the-art mIoUs of 83.5% on Cityscapes and 82.9% on CamVid. ; Customizable: Gamification platform must be easy to customise and tailor the design uniquely to have a look and the feel of the specific business. In this task, both 2D and 3D parameters are evaluated. Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This work presents three main novelties: the first is an Improved Guided Upsampling Module that can replace in toto the decoder part in common semantic segmentation networks. In this work, we ask if we may leverage semi-supervised learning in unlabeled video sequences to improve the performance on urban scene segmentation, simultaneously tackling semantic, instance, and panoptic segmentation. The DSNet demonstrates a good trade-off between accuracy and speed. From Recognition to Cognition: Visual Commonsense Reasoning (VCR) Rowan Zellers, Yonatan Bisk, Ali Farhadi, Yejin Choi. https://ivankreso.github.io/publication/ladder-densenet/. First, we pre-train "HRNet+OCR" method on the Mapillary training set (achieves 50.8% on the Mapillary val set). image net) and post-processing, single model, no post-processing with CRFs, Christian Szegedy , Wei Liu , Yangqing Jia , Pierre Sermanet , Scott Reed , Dragomir Anguelov , Dumitru Erhan , Vincent Vanhoucke , Andrew Rabinovich, Eduardo Romera, Jose M. Alvarez, Luis M. Bergasa and Roberto Arroyo, Transactions on Intelligent Transportation Systems (T-ITS), ERFNet pretrained on ImageNet and trained only on the fine train (2975) annotated images, ERFNet trained entirely on the fine train set (2975 images) without any pretraining nor coarse labels, Here we show how to improve pixel-wise semantic segmentation by manipulating convolution-related operations that are better for practical use. Shu Liu, Jiaya Jia, Sanja Fidler, Raquel Urtasun. The proposed method possesses several advantages. Both are described in the paper. AdaptIS generates pixel-accurate object masks, therefore it accurately segments objects of complex shape or severely occluded ones. Using our approach we achieve a new state-of-the-art results in both Mapillary (61.1 IOU val) and Cityscapes (85.4 IOU test). To tackle this task, also known as "Panoptic Segmentation", we take advantage of a novel segmentation head that seamlessly integrates multi-scale features generated by a Feature Pyramid Network with contextual information conveyed by a light-weight DeepLab-like module. KittiBox: A car detection model implemented in Tensorflow. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. This submission is trained on coarse+fine(train+val set, 2975+500 images). Constructing viable search spaces in this domain is challenging because of the multi-scale representation of visual information and the necessity to operate on high resolution imagery. Experiments on popular segmentation benchmarks demonstrate the competency of FasterSeg. Default is 35 MB. Besides, our method also could improve the results of PointRend and PANet by more than 1.0% without any re-training or fine-tuning the segmentation models. LDFNet achieves very competitive results compared to the other state-of-art systems on the challenging Cityscapes dataset, while it maintains an inference speed faster than most of the existing top-performing networks. Semantic segmentation has achieved remarkable progress but remains challenging due to the complex scene, object occlusion, and so on. The loss weight map is then applied to segmentation loss, with the goal of learning a more robust model by paying more attention to the hard pixels. Trees are instanced so saving little bit of texture memory wouldn't yield great performance boost but it can't be worse. Ladder DenseNet-121 trained on train+val, fine labels only. There are multiple branches with different dilate rates for varied pooling size, thus varying receptive field. In this work, we propose a Semantic Prediction Guidance (SPG) module which learns to re-weight the local features through the guidance from pixel-wise semantic prediction. Specifically, we achieve 76.6% and 75.9% mIOU on Cityscapes validation and test sets respectively, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. Xuhong Li, Yves Grandvalet, Franck Davoine. Xingqian Xu, Mangtik Chiu, Thomas Huang, Honghui Shi. Multiple images scales are passed through a network and then the results are combined with averaging or max pooling. CIFAR-10 is another multi-class classification challenge where accuracy matters. The performance of our network is evaluated on three different tasks: (1) object classification, (2) semantic segmentation, and (3) language modeling. The model is DeepLab v3+ backend on SEResNeXt50. However, few efforts have been attempted to bring this effective design to semantic segmentation. The final scores, iIoUcategory and iIoUclass, are obtained as the means for the two semantic granularities. First, we employ convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool to repurpose ResNet-101 (trained on image classification task) in dense prediction tasks. This framework 1) effectively enlarges the receptive fields of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation. Seamless Scene Segmentation is a CNN-based architecture that can be trained end-to-end to predict a complete class- and instance-specific labeling for each pixel in an image. Then, only the fine-annotated Cityscapes dataset (2975 training images) is used to train the complete DSNet. To handle this issue, in this paper, we propose a novel deep neural network named RelationNet, which utilizes CNN and RNN to aggregate context information. We used the margin calibration with log-loss as the learning objective. ADSCNet: Asymmetric Depthwise Separable Convolution for Semantic Segmentation in Real-time. Since the rise in autonomous systems, real-time computation is increasingly desirable. Xiangtai Li, Xia Li, Li Zhang, Guangliang Cheng, Jianping Shi, Zhouchen Lin, Shaohua Tan, and Yunhai Tong. Take A Sneak Peak At The Movies Coming Out This Week (8/12) These NFL players use their star power to make a difference; Weekend Movie Releases – February 5th – February 7th Used as the instance head, 2975+500 images ) without adding the validation set masks, it! Annotation practices algorithms usually compromise on restricted search space, but becomes increasingly problematic for dense image prediction which a.: MMDetection: Open MMLab detection toolbox and benchmark, intro: bounding box keypoints within the network was retrained! 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