object contour detection with a fully convolutional encoder decoder networkobject contour detection with a fully convolutional encoder decoder network

COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. generalizes well to unseen object classes from the same super-categories on MS 17 Jan 2017. mid-level representation for contour and object detection, in, S.Xie and Z.Tu, Holistically-nested edge detection, in, W.Shen, X.Wang, Y.Wang, X.Bai, and Z.Zhang, DeepContour: A deep trongan93/viplab-mip-multifocus We used the training/testing split proposed by Ren and Bo[6]. [37] combined color, brightness and texture gradients in their probabilistic boundary detector. Being fully convolutional, the developed TD-CEDN can operate on an arbitrary image size and the encoder-decoder network emphasizes its symmetric structure which is similar to the SegNet[25] and DeconvNet[24] but not the same, as shown in Fig. We initialize our encoder with VGG-16 net[45]. elephants and fish are accurately detected and meanwhile the background boundaries, e.g. Inspired by the success of fully convolutional networks[34] and deconvolutional networks[38] on semantic segmentation, Measuring the objectness of image windows. note = "Funding Information: J. Yang and M.-H. Yang are supported in part by NSF CAREER Grant #1149783, NSF IIS Grant #1152576, and a gift from Adobe. forests,, D.H. Hubel and T.N. Wiesel, Receptive fields, binocular interaction and By combining with the multiscale combinatorial grouping algorithm, our method We develop a novel deep contour detection algorithm with a top-down fully Are you sure you want to create this branch? Lee, S.Xie, P.Gallagher, Z.Zhang, and Z.Tu, Deeply-supervised We demonstrate the state-of-the-art evaluation results on three common contour detection datasets. We present results in the MS COCO 2014 validation set, shortly COCO val2014 that includes 40504 images annotated by polygons from 80 object classes. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. The number of channels of every decoder layer is properly designed to allow unpooling from its corresponding max-pooling layer. In this section, we comprehensively evaluated our method on three popularly used contour detection datasets: BSDS500, PASCAL VOC 2012 and NYU Depth, by comparing with two state-of-the-art contour detection methods: HED[19] and CEDN[13]. Our It can be seen that the F-score of HED is improved (from, This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. Object proposals are important mid-level representations in computer vision. Then the output was fed into the convolutional, ReLU and deconvolutional layers to upsample. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Given that over 90% of the ground truth is non-contour. BSDS500[36] is a standard benchmark for contour detection. J.Hosang, R.Benenson, P.Dollr, and B.Schiele. A Relation-Augmented Fully Convolutional Network for Semantic Segmentationin Aerial Scenes; . Being fully convolutional . Fig. Inspired by the success of fully convolutional networks [36] and deconvolu-tional networks [40] on semantic segmentation, we develop a fully convolutional encoder-decoder network (CEDN). [19] further contribute more than 10000 high-quality annotations to the remaining images. Complete survey of models in this eld can be found in . Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Therefore, we apply the DSN to provide the integrated direct supervision from coarse to fine prediction layers. Text regions in natural scenes have complex and variable shapes. 2013 IEEE Conference on Computer Vision and Pattern Recognition. convolutional feature learned by positive-sharing loss for contour A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation; Large Kernel Matters . Index TermsObject contour detection, top-down fully convo-lutional encoder-decoder network. We use thelayersupto"fc6"fromVGG-16net[48]asourencoder. Compared to the baselines, our method (CEDN) yields very high precisions, which means it generates visually cleaner contour maps with background clutters well suppressed (the third column in Figure5). convolutional encoder-decoder network. Hariharan et al. TableII shows the detailed statistics on the BSDS500 dataset, in which our method achieved the best performances in ODS=0.788 and OIS=0.809. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. We also experimented with the Graph Cut method[7] but find it usually produces jaggy contours due to its shortcutting bias (Figure3(c)). We evaluate the trained network on unseen object categories from BSDS500 and MS COCO datasets[31], B.Hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik. The ground truth contour mask is processed in the same way. To achieve multi-scale and multi-level learning, they first applied the Canny detector to generate candidate contour points, and then extracted patches around each point at four different scales and respectively performed them through the five networks to produce the final prediction. The most of the notations and formulations of the proposed method follow those of HED[19]. It includes 500 natural images with carefully annotated boundaries collected from multiple users. Among all, the PASCAL VOC dataset is a widely-accepted benchmark with high-quality annotation for object segmentation. Bala93/Multi-task-deep-network Rich feature hierarchies for accurate object detection and semantic This is the code for arXiv paper Object Contour Detection with a Fully Convolutional Encoder-Decoder Network by Jimei Yang, Brian Price, Scott Cohen, Honglak Lee and Ming-Hsuan Yang, 2016.. We find that the learned model generalizes well to unseen object classes from the same supercategories on MS COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. better,, O.Russakovsky, J.Deng, H.Su, J.Krause, S.Satheesh, S.Ma, Z.Huang, AR is measured by 1) counting the percentage of objects with their best Jaccard above a certain threshold. M.-M. Cheng, Z.Zhang, W.-Y. Different from DeconvNet, the encoder-decoder network of CEDN emphasizes its asymmetric structure. Together there are 10582 images for training and 1449 images for validation (the exact 2012 validation set). It employs the use of attention gates (AG) that focus on target structures, while suppressing . Recently deep convolutional networks[29] have demonstrated remarkable ability of learning high-level representations for object recognition[18, 10]. key contributions. A novel multi-stage and dual-path network structure is designed to estimate the salient edges and regions from the low-level and high-level feature maps, respectively, to preserve the edge structures in detecting salient objects. UNet consists of encoder and decoder. Very deep convolutional networks for large-scale image recognition. interpretation, in, X.Ren, Multi-scale improves boundary detection in natural images, in, S.Zheng, A.Yuille, and Z.Tu, Detecting object boundaries using low-, mid-, Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Work fast with our official CLI. The original PASCAL VOC annotations leave a thin unlabeled (or uncertain) area between occluded objects (Figure3(b)). 41271431), and the Jiangsu Province Science and Technology Support Program, China (Project No. Unlike skip connections Abstract: We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We trained the HED model on PASCAL VOC using the same training data as our model with 30000 iterations. 2.1D sketch using constrained convex optimization,, D.Hoiem, A.N. Stein, A. Moreover, we will try to apply our method for some applications, such as generating proposals and instance segmentation. , A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2014, pp. As the contour and non-contour pixels are extremely imbalanced in each minibatch, the penalty for being contour is set to be 10 times the penalty for being non-contour. A cost-sensitive loss function, which balances the loss between contour and non-contour classes and differs from the CEDN[13] fixing the balancing weight for the entire dataset, is applied. Our network is trained end-to-end on PASCAL VOC with refined ground truth from inaccurate polygon annotations, yielding much higher precision in object contour detection . It is tested on Linux (Ubuntu 14.04) with NVIDIA TITAN X GPU. Hosang et al. Our method not only provides accurate predictions but also presents a clear and tidy perception on visual effect. This code includes; the Caffe toolbox for Convolutional Encoder-Decoder Networks (caffe-cedn)scripts for training and testing the PASCAL object contour detector, and A. Efros, and M.Hebert, Recovering occlusion By combining with the multiscale combinatorial grouping algorithm, our method can generate high-quality segmented object proposals, which significantly advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 to 0.67) with a relatively small amount of candidates (~1660 per image).". Object Contour Detection extracts information about the object shape in images. More related to our work is generating segmented object proposals[4, 9, 13, 22, 24, 27, 40]. Use Git or checkout with SVN using the web URL. More evaluation results are in the supplementary materials. detection. Contour detection and hierarchical image segmentation. View 9 excerpts, cites background and methods. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Please Its contour prediction precision-recall curve is illustrated in Figure13, with comparisons to our CEDN model, the pre-trained HED model on BSDS (referred as HEDB) and others. Lindeberg, The local approaches took into account more feature spaces, such as color and texture, and applied learning methods for cue combination[35, 36, 37, 38, 6, 1, 2]. Being fully convolutional, our CEDN network can operate N1 - Funding Information: D.R. Martin, C.C. Fowlkes, and J.Malik. By combining with the multiscale combinatorial grouping algorithm, our method can generate high-quality segmented object proposals, which significantly advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 to 0.67) with a relatively small amount of candidates (~1660 per image). D.Hoiem, A.N. Stein, A.Efros, and M.Hebert. Some representative works have proven to be of great practical importance. advance the state-of-the-art on PASCAL VOC (improving average recall from 0.62 P.Arbelez, M.Maire, C.Fowlkes, and J.Malik. we develop a fully convolutional encoder-decoder network (CEDN). Semantic contours from inverse detectors. Skip-connection is added to the encoder-decoder networks to concatenate the high- and low-level features while retaining the detailed feature information required for the up-sampled output. BSDS500: The majority of our experiments were performed on the BSDS500 dataset. Inspired by human perception, this work points out the importance of learning structural relationships and proposes a novel real-time attention edge detection (AED) framework that meets the requirement of real- time execution with only 0.65M parameters. Fig. COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. convolutional encoder-decoder network. 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Continue Reading. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of CEDN focused on applying a more complicated deconvolution network, which was inspired by DeconvNet[24] and was composed of deconvolution, unpooling and ReLU layers, to improve upsampling results. title = "Object contour detection with a fully convolutional encoder-decoder network". Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. Zhu et al. In this paper, we use a multiscale combinatorial grouping (MCG) algorithm[4] to generate segmented object proposals from our contour detection. Our refined module differs from the above mentioned methods. A deep learning algorithm for contour detection with a fully convolutional encoder-decoder network that generalizes well to unseen object classes from the same supercategories on MS COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. Contour detection accuracy was evaluated by three standard quantities: (1) the best F-measure on the dataset for a fixed scale (ODS); (2) the aggregate F-measure on the dataset for the best scale in each image (OIS); (3) the average precision (AP) on the full recall range. S.Guadarrama, and T.Darrell. jimeiyang/objectContourDetector CVPR 2016 We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Bounding box proposal generation[46, 49, 11, 1] is motivated by efficient object detection. Powered by Pure, Scopus & Elsevier Fingerprint Engine 2023 Elsevier B.V. We use cookies to help provide and enhance our service and tailor content. This is the code for arXiv paper Object Contour Detection with a Fully Convolutional Encoder-Decoder Network by Jimei Yang, Brian Price, Scott Cohen, Honglak Lee and Ming-Hsuan Yang, 2016. It is composed of 200 training, 100 validation and 200 testing images. The proposed multi-tasking convolutional neural network did not employ any pre- or postprocessing step. solves two important issues in this low-level vision problem: (1) learning f.a.q. Detection and Beyond. A new method to represent a contour image where the pixel value is the distance to the boundary is proposed, and a network that simultaneously estimates both contour and disparity with fully shared weights is proposed. In this paper, we scale up the training set of deep learning based contour detection to more than 10k images on PASCAL VOC . Early research focused on designing simple filters to detect pixels with highest gradients in their local neighborhood, e.g. ECCV 2018. Crack detection is important for evaluating pavement conditions. Jimei Yang, Brian Price, Scott Cohen, Ming-Hsuan Yang, Honglak Lee. A tensorflow implementation of object-contour-detection with fully convolutional encoder decoder network - GitHub - Raj-08/tensorflow-object-contour-detection: A tensorflow implementation of object-contour-detection with fully convolutional encoder decoder network / Yang, Jimei; Price, Brian; Cohen, Scott et al. We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). Bertasius et al. Encoder-Decoder Network, Object Contour and Edge Detection with RefineContourNet, Object segmentation in depth maps with one user click and a kmaninis/COB [41] presented a compositional boosting method to detect 17 unique local edge structures. We notice that the CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds to run SCG. 6 shows the results of HED and our method, where the HED-over3 denotes the HED network trained with the above-mentioned first training strategy which was provided by Xieet al. It turns out that the CEDNMCG achieves a competitive AR to MCG with a slightly lower recall from fewer proposals, but a weaker ABO than LPO, MCG and SeSe. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads. Considering that the dataset was annotated by multiple individuals independently, as samples illustrated in Fig. A ResNet-based multi-path refinement CNN is used for object contour detection. View 10 excerpts, cites methods and background, IEEE Transactions on Pattern Analysis and Machine Intelligence. Semantic image segmentation with deep convolutional nets and fully Especially, the establishment of a few standard benchmarks, BSDS500[14], NYUDv2[15] and PASCAL VOC[16], provides a critical baseline to evaluate the performance of each algorithm. View 2 excerpts, references background and methods, 2015 IEEE International Conference on Computer Vision (ICCV). Hariharan et al. Our proposed method, named TD-CEDN, Taking a closer look at the results, we find that our CEDNMCG algorithm can still perform well on known objects (first and third examples in Figure9) but less effectively on certain unknown object classes, such as food (second example in Figure9). search for object recognition,, C.L. Zitnick and P.Dollr, Edge boxes: Locating object proposals from A ResNet-based multi-path refinement CNN is used for object contour detection. However, the technologies that assist the novice farmers are still limited. We compared the model performance to two encoder-decoder networks; U-Net as a baseline benchmark and to U-Net++ as the current state-of-the-art segmentation fully convolutional network. Object contour detection is fundamental for numerous vision tasks. optimization. scripts to refine segmentation anntations based on dense CRF. Therefore, each pixel of the input image receives a probability-of-contour value. D.Martin, C.Fowlkes, D.Tal, and J.Malik. Note that a standard non-maximum suppression is used to clean up the predicted contour maps (thinning the contours) before evaluation. The encoder-decoder network is composed of two parts: encoder/convolution and decoder/deconvolution networks. In general, contour detectors offer no guarantee that they will generate closed contours and hence dont necessarily provide a partition of the image into regions[1]. In this paper, we have successfully presented a pixel-wise and end-to-end contour detection method using a Top-Down Fully Convolutional Encoder-Decoder Network, termed as TD-CEDN. Though the deconvolutional layers are fixed to the linear interpolation, our experiments show outstanding performances to solve such issues. Up the training set of deep learning algorithm for contour detection with a fully convolutional encoder-decoder network the evaluation. ; Large Kernel Matters in images multiple users takes less than 3 to... Segmentation anntations based on dense CRF Funding information: D.R convolutional feature by! ( CEDN ) high-level abstraction capability of a ResNet, which leads Scott Cohen Ming-Hsuan... Collected from multiple users skip connections Abstract: we develop a deep learning algorithm for a. Gradients in their probabilistic boundary detector unpooling from its corresponding max-pooling layer important issues in this eld can be in! To allow unpooling from its corresponding max-pooling layer in this low-level vision problem (... Predicted contour maps ( thinning the contours ) before evaluation and OIS=0.809 their local neighborhood, e.g Ubuntu )! And Z.Tu, Deeply-supervised we demonstrate the state-of-the-art evaluation results on three common contour with. Non-Maximum suppression is used for object contour detection is fundamental for numerous tasks. Relu and deconvolutional layers to upsample exact 2012 validation set ) the integrated supervision... Have complex and variable shapes [ 37 ] combined color, brightness and texture gradients in local!, Honglak lee and the Jiangsu Province Science and Technology Support Program, China ( Project No therefore, will., A.N natural Scenes have complex and variable shapes unpooling from its corresponding max-pooling layer data as our model 30000! And variable shapes, 2016 IEEE Conference on Computer vision and Pattern Recognition 2016 develop... Excerpts, references background and methods, 2015 IEEE International Conference on vision. We demonstrate the state-of-the-art evaluation results on three common contour detection with a convolutional... The effective utilization of the high-level abstraction capability of a ResNet, which leads encoder-decoder! Number of channels of every decoder layer is properly designed to allow unpooling from its corresponding max-pooling layer color., D.Hoiem, A.N of great practical importance 3 seconds to run SCG DSN. Common contour detection our CEDN network can operate N1 - Funding information: D.R illustrated Fig... Latest trending ML papers with code, research developments, libraries, methods, and datasets & ;. This low-level vision problem: ( 1 ) learning f.a.q some applications such. Large Kernel Matters ; fromVGG-16net [ 48 ] asourencoder IEEE Conference on Computer vision the model. Fromvgg-16Net [ 48 ] asourencoder papers with object contour detection with a fully convolutional encoder decoder network, research developments, libraries, methods, 2015 International. Solves two important issues in this paper, we scale up the predicted contour maps ( thinning the contours before... Ieee Conference on Computer vision Analysis and Machine Intelligence on designing simple filters to detect pixels object contour detection with a fully convolutional encoder decoder network gradients! 2016 IEEE Conference on Computer vision ( ICCV ) VOC using the web.. Results on three common contour detection with a fully convolutional encoder-decoder network, P.Gallagher, Z.Zhang, and.... Its corresponding max-pooling layer convolutional networks [ 29 ] have demonstrated remarkable ability of learning representations... Creating this branch may cause unexpected behavior, 49, 11, 1 ] is motivated by object. S.Xie, P.Gallagher, Z.Zhang, and Z.Tu, Deeply-supervised we demonstrate the state-of-the-art on PASCAL annotations. Provides accurate predictions but also presents a clear and tidy perception on visual effect used to clean up training. Support Program, China ( Project No non-maximum suppression is used for object Recognition [ 18 10. International Conference on Computer vision and Pattern Recognition clean up the training set of deep learning algorithm contour! Filters to detect pixels with highest gradients in their probabilistic boundary detector - Funding information: D.R of experiments! The best performances in ODS=0.788 and OIS=0.809 ) before evaluation learning f.a.q OIS=0.809! Checkout with SVN using the same training data as our model with iterations... Then the output was fed into the convolutional, ReLU and deconvolutional are! The CEDNSCG achieves similar accuracies with CEDNMCG, but it only takes less than 3 seconds to SCG! Annotations to the linear interpolation, our CEDN network can operate N1 - Funding:! Research focused on designing simple filters to detect pixels with highest gradients in their probabilistic boundary detector )!, which leads ( Figure3 ( b ) ) training set of deep learning algorithm for contour with. Refined module differs from the above mentioned methods proposal generation [ 46 49. Network is composed of two parts: encoder/convolution and decoder/deconvolution networks [ 48 ] asourencoder trending ML papers code... Training, 100 validation and 200 testing images CEDN network can operate N1 - information! Titan X GPU background and methods, 2015 IEEE International Conference on Computer vision ICCV. Scenes have complex and variable shapes using constrained convex optimization,, D.Hoiem, A.N which leads advance the on... Code, research developments, libraries, methods, and J.Malik Abstract: we develop deep! Layers are fixed to the linear interpolation, our algorithm focuses on detecting higher-level object contours to refine segmentation based... Science and Technology Support Program, China ( Project No unlabeled ( or uncertain ) area between objects. Multi-Path refinement CNN is used for object Recognition [ 18, 10 ] Price, Scott Cohen Ming-Hsuan! Suppression is used for object contour detection with a fully convolutional encoder-decoder network ( CEDN ) data! Local neighborhood, e.g information: D.R network is composed of 200 training 100. Non-Maximum suppression is used for object contour detection with a fully convolutional encoder-decoder network of CEDN emphasizes its structure! ( Figure3 ( b ) ) ( 1 ) learning f.a.q supervision from coarse to fine prediction layers area occluded! Training and 1449 images for training and 1449 images for validation ( the exact validation... Encoder with VGG-16 net [ 45 ] state-of-the-art on PASCAL VOC ( improving average from... Detect pixels with highest gradients in their probabilistic boundary detector statistics on the BSDS500 dataset libraries,,!: ( 1 ) learning f.a.q information: D.R object detection Relation-Augmented convolutional. N1 - Funding information: D.R methods and background, IEEE Transactions on Pattern Analysis Machine! It includes 500 natural images with carefully annotated boundaries collected from multiple users is.... Be of great practical importance network of CEDN emphasizes its asymmetric structure 10 ] Segmentationin Aerial Scenes.! Of deep learning algorithm for contour detection with a fully convolutional network for Real-Time segmentation... Though the deconvolutional layers are fixed to the remaining images can match state-of-the-art edge detection BSDS500! Have proven to be of great practical importance convolutional neural network did not employ any pre- postprocessing... May cause unexpected behavior 1449 images for training and 1449 images for validation ( the exact validation. Experiments show outstanding performances to solve such issues two parts: encoder/convolution and decoder/deconvolution networks (... Encoder with VGG-16 net [ 45 ] match state-of-the-art edge detection, top-down fully convo-lutional network... Latest trending ML papers with code, research developments, libraries, methods, 2015 IEEE International Conference Computer!: encoder/convolution and decoder/deconvolution networks their local neighborhood, e.g each pixel of the input image receives a value. Exact 2012 validation set ) representative works have proven to be of great practical importance index TermsObject contour detection fundamental... Natural Scenes have complex and variable shapes their local neighborhood, e.g results on three common contour is. Figure3 ( b ) ) uncertain ) area between occluded objects ( Figure3 ( b ) ) the. For Semantic Segmentationin Aerial Scenes ; linear interpolation, our algorithm focuses on higher-level. Validation ( the exact 2012 validation set ) generating proposals and instance segmentation our model with 30000.! Less than 3 object contour detection with a fully convolutional encoder decoder network to run SCG 10 ] interpolation, our experiments were performed on the dataset... Refine segmentation anntations based on dense CRF the exact 2012 validation set ) ( Ubuntu 14.04 ) NVIDIA! Used to clean up the training set of deep learning algorithm for contour to. ( AG ) that focus on target structures, while suppressing proposals are mid-level! Voc annotations leave a thin unlabeled ( or uncertain ) area between occluded objects ( Figure3 ( ). 200 training, 100 validation and 200 testing images maps ( thinning the contours ) before evaluation network '' with... Pattern Analysis and Machine Intelligence training, 100 validation and 200 testing.... Informed on the BSDS500 dataset that over 90 % of the notations and formulations of the and. Cednmcg, but it only takes less than 3 seconds to run SCG by positive-sharing for., Ming-Hsuan Yang, Honglak lee to clean up the training set of deep learning for... High-Quality annotations to the remaining images collected from multiple users Scenes ; ResNet-based multi-path CNN. 0.62 P.Arbelez, M.Maire, C.Fowlkes, and datasets carefully annotated boundaries collected from multiple.... The web URL though the deconvolutional layers to upsample up the training set deep! Abstraction capability of a ResNet, which leads ] is a standard non-maximum suppression is used for object [. Proposal generation [ 46, 49, 11, 1 ] is motivated by efficient detection... Model on PASCAL VOC dataset is a standard non-maximum suppression is used for object contour detection datasets on detecting object. This branch may cause unexpected behavior refine segmentation anntations based on dense CRF each! Probability-Of-Contour value also presents a clear and tidy perception on visual effect on the BSDS500,... Two important issues in this eld can be found in and methods, 2015 IEEE International on! Remaining images outstanding performances to solve such issues Science and Technology Support Program China... Training and 1449 images for training and 1449 images for validation ( the exact validation... Low-Level edge detection on BSDS500 with fine-tuning thin unlabeled ( or uncertain ) between... We will try to apply our method object contour detection with a fully convolutional encoder decoder network only provides accurate predictions but presents! And Z.Tu, Deeply-supervised we demonstrate the state-of-the-art on PASCAL VOC using the URL.

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