Arbelaez et al. edges, in, V.Ferrari, L.Fevrier, F.Jurie, and C.Schmid, Groups of adjacent contour This code includes; the Caffe toolbox for Convolutional Encoder-Decoder Networks (caffe-cedn)scripts for training and testing the PASCAL object contour detector, and Bibliographic details on Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. We fine-tuned the model TD-CEDN-over3 (ours) with the VOC 2012 training dataset. [48] used a traditional CNN architecture, which applied multiple streams to integrate multi-scale and multi-level features, to achieve contour detection. Early approaches to contour detection[31, 32, 33, 34] aim at quantifying the presence of boundaries through local measurements, which is the key stage of designing detectors. We find that the learned model . BING: Binarized normed gradients for objectness estimation at Caffe: Convolutional architecture for fast feature embedding. Our proposed method, named TD-CEDN, . Object Contour Detection with a Fully Convolutional Encoder-Decoder Network . TD-CEDN performs the pixel-wise prediction by Generating object segmentation proposals using global and local Quantitatively, we present per-class ARs in Figure12 and have following observations: CEDN obtains good results on those classes that share common super-categories with PASCAL classes, such as vehicle, animal and furniture. Hariharan et al. Moreover, to suppress the image-border contours appeared in the results of CEDN, we applied a simple image boundary region extension method to enlarge the input image 10 pixels around the image during the testing stage. Given the success of deep convolutional networks [29] for . Each side-output can produce a loss termed Lside. [37] combined color, brightness and texture gradients in their probabilistic boundary detector. detection, our algorithm focuses on detecting higher-level object contours. Recently, deep learning methods have achieved great successes for various applications in computer vision, including contour detection[20, 48, 21, 22, 19, 13]. Wu et al. A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation; Large Kernel Matters . We believe our instance-level object contours will provide another strong cue for addressing this problem that is worth investigating in the future. FCN[23] combined the lower pooling layer with the current upsampling layer following by summing the cropped results and the output feature map was upsampled. Note that we fix the training patch to. 3 shows the refined modules of FCN[23], SegNet[25], SharpMask[26] and our proposed TD-CEDN. Conditional random fields as recurrent neural networks. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks. The number of channels of every decoder layer is properly designed to allow unpooling from its corresponding max-pooling layer. Directly using contour coordinates to describe text regions will make the modeling inadequate and lead to low accuracy of text detection. We also propose a new joint loss function for the proposed architecture. The key contributions are summarized below: We develop a simple yet effective fully convolutional encoder-decoder network for object contour prediction and the trained model generalizes well to unseen object classes from the same super-categories, yielding significantly higher precision in object contour detection than previous methods. The Pascal visual object classes (VOC) challenge. RIGOR: Reusing inference in graph cuts for generating object contours from inverse detectors, in, S.Gupta, R.Girshick, P.Arbelez, and J.Malik, Learning rich features Recovering occlusion boundaries from a single image. 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. with a common multi-scale convolutional architecture, in, B.Hariharan, P.Arbelez, R.Girshick, and J.Malik, Hypercolumns for Kivinen et al. can generate high-quality segmented object proposals, which significantly Our goal is to overcome this limitation by automatically converting an existing deep contour detection model into a salient object detection model without using any manual salient object masks. 0 benchmarks contour detection than previous methods. f.a.q. P.Rantalankila, J.Kannala, and E.Rahtu. This work was partially supported by the National Natural Science Foundation of China (Project No. To address the quality issue of ground truth contour annotations, we develop a dense CRF[26] based method to refine the object segmentation masks from polygons. The network architecture is demonstrated in Figure2. Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation Tianyu He, Xu Tan, Yingce Xia, Di He, . Download the pre-processed dataset by running the script, Download the VGG16 net for initialization by running the script, Test the learned network by running the script, Download the pre-trained model by running the script. deep network for top-down contour detection, in, J. We develop a simple yet effective fully convolutional encoder-decoder network for object contour detection and the trained model generalizes well to unseen object classes from the same super-categories, yielding significantly higher precision than previous methods. Inspired by the success of fully convolutional networks[34] and deconvolutional networks[38] on semantic segmentation, If nothing happens, download Xcode and try again. Very deep convolutional networks for large-scale image recognition. We experiment with a state-of-the-art method of multiscale combinatorial grouping[4] to generate proposals and believe our object contour detector can be directly plugged into most of these algorithms. The final prediction also produces a loss term Lpred, which is similar to Eq. 6. These observations urge training on COCO, but we also observe that the polygon annotations in MS COCO are less reliable than the ones in PASCAL VOC (third example in Figure9(b)). The encoder-decoder network is composed of two parts: encoder/convolution and decoder/deconvolution networks. boundaries, in, , Imagenet large scale 7 shows the fused performances compared with HED and CEDN, in which our method achieved the state-of-the-art performances. Please follow the instructions below to run the code. Ming-Hsuan Yang. N2 - We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Fig. Yang et al. The goal of our proposed framework is to learn a model that minimizes the differences between prediction of the side output layer and the ground truth. We formulate contour detection as a binary image labeling problem where 1 and 0 indicates contour and non-contour, respectively. We propose a convolutional encoder-decoder framework to extract image contours supported by a generative adversarial network to improve the contour quality. hierarchical image structures, in, P.Kontschieder, S.R. Bulo, H.Bischof, and M.Pelillo, Structured A tag already exists with the provided branch name. Note that the occlusion boundaries between two instances from the same class are also well recovered by our method (the second example in Figure5). 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 than previous methods. Use Git or checkout with SVN using the web URL. However, since it is very challenging to collect high-quality contour annotations, the available datasets for training contour detectors are actually very limited and in small scale. home. We initialize our encoder with VGG-16 net[45]. It is tested on Linux (Ubuntu 14.04) with NVIDIA TITAN X GPU. More evaluation results are in the supplementary materials. We find that the learned model . Being fully convolutional, our CEDN network can operate on arbitrary image size and the encoder-decoder network emphasizes its asymmetric structure that differs from deconvolutional network[38]. 111HED pretrained model:http://vcl.ucsd.edu/hed/, TD-CEDN-over3 and TD-CEDN-all refer to the proposed TD-CEDN trained with the first and second training strategies, respectively. Being fully convolutional, our CEDN network can operate training by reducing internal covariate shift,, C.-Y. P.Dollr, and C.L. Zitnick. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Given its axiomatic importance, however, we find that object contour detection is relatively under-explored in the literature. It is likely because those novel classes, although seen in our training set (PASCAL VOC), are actually annotated as background. title = "Object contour detection with a fully convolutional encoder-decoder network". B.C. Russell, A.Torralba, K.P. Murphy, and W.T. Freeman. We trained the HED model on PASCAL VOC using the same training data as our model with 30000 iterations. Contents. 2015BAA027), the National Natural Science Foundation of China (Project No. Formulate object contour detection as an image labeling problem. 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. yielding much higher precision in object contour detection than previous methods. Bala93/Multi-task-deep-network Our predictions present the object contours more precisely and clearly on both statistical results and visual effects than the previous networks. In each decoder stage, its composed of upsampling, convolutional, BN and ReLU layers. and P.Torr. UNet consists of encoder and decoder. Fig. aware fusion network for RGB-D salient object detection. -CEDN1vgg-16, dense CRF, encoder VGG decoder1simply the pixel-wise logistic loss, low-levelhigher-levelEncoder-Decoderhigher-levelsegmented object proposals, F-score = 0.57F-score = 0.74. Indoor segmentation and support inference from rgbd images. They formulate a CRF model to integrate various cues: color, position, edges, surface orientation and depth estimates. Previous algorithms efforts lift edge detection to a higher abstract level, but still fall below human perception due to their lack of object-level knowledge. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. I. to use Codespaces. B.Hariharan, P.Arbelez, L.Bourdev, S.Maji, and J.Malik. of indoor scenes from RGB-D images, in, J.J. Lim, C.L. Zitnick, and P.Dollr, Sketch tokens: A learned The decoder maps the encoded state of a fixed . BE2014866). We develop a novel deep contour detection algorithm with a top-down fully . For example, it can be used for image segmentation[41, 3], for object detection[15, 18], and for occlusion and depth reasoning[20, 2]. UR - http://www.scopus.com/inward/record.url?scp=84986265719&partnerID=8YFLogxK, UR - http://www.scopus.com/inward/citedby.url?scp=84986265719&partnerID=8YFLogxK, T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, T2 - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. The encoder network consists of 13 convolutional layers which correspond to the first 13 convolutional layers in the VGG16 network designed for object classification. Different from previous low-level edge refers to the image-level loss function for the side-output. Recently, the supervised deep learning methods, such as deep Convolutional Neural Networks (CNNs), have achieved the state-of-the-art performances in such field, including, In this paper, we develop a pixel-wise and end-to-end contour detection system, Top-Down Convolutional Encoder-Decoder Network (TD-CEDN), which is inspired by the success of Fully Convolutional Networks (FCN)[23], HED, Encoder-Decoder networks[24, 25, 13] and the bottom-up/top-down architecture[26]. Detection and Beyond. COCO and can match state-of-the-art edge detection on BSDS500 with fine-tuning. RGB-D Salient Object Detection via 3D Convolutional Neural Networks Qian Chen1, Ze Liu1, . The above proposed technologies lead to a more precise and clearer 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. The main problem with filter based methods is that they only look at the color or brightness differences between adjacent pixels but cannot tell the texture differences in a larger receptive field. To find the high-fidelity contour ground truth for training, we need to align the annotated contours with the true image boundaries. Several example results are listed in Fig. The above mentioned four methods[20, 48, 21, 22] are all patch-based but not end-to-end training and holistic image prediction networks. 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. a fully convolutional encoder-decoder network (CEDN). Early research focused on designing simple filters to detect pixels with highest gradients in their local neighborhood, e.g. NeurIPS 2018. V.Ferrari, L.Fevrier, F.Jurie, and C.Schmid. Edit social preview. 2.1D sketch using constrained convex optimization,, D.Hoiem, A.N. Stein, A. 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). Semi-Supervised Video Salient Object Detection Using Pseudo-Labels; Contour Loss: Boundary-Aware Learning for Salient Object Segmentation . Dropout: a simple way to prevent neural networks from overfitting,, Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J. Our During training, we fix the encoder parameters and only optimize the decoder parameters. Since visually salient edges correspond to variety of visual patterns, designing a universal approach to solve such tasks is difficult[10]. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. We show we can fine tune our network for edge detection and match the state-of-the-art in terms of precision and recall. color, and texture cues. Our proposed algorithm achieved the state-of-the-art on the BSDS500 Their integrated learning of hierarchical features was in distinction to previous multi-scale approaches. quality dissection. Bertasius et al. Long, R.Girshick, 13 papers with code [19], a number of properties, which are key and likely to play a role in a successful system in such field, are summarized: (1) carefully designed detector and/or learned features[36, 37], (2) multi-scale response fusion[39, 2], (3) engagement of multiple levels of visual perception[11, 12, 49], (4) structural information[18, 10], etc. visual attributes (e.g., color, node shape, node size, and Zhou [11] used an encoder-decoder network with an location of the nodes). Therefore, we apply the DSN to provide the integrated direct supervision from coarse to fine prediction layers. Fig. 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 . As a result, the trained model yielded high precision on PASCAL VOC and BSDS500, and has achieved comparable performance with the state-of-the-art on BSDS500 after fine-tuning. BSDS500[36] is a standard benchmark for contour detection. (up to the fc6 layer) and to achieve dense prediction of image size our decoder is constructed by alternating unpooling and convolution layers where unpooling layers re-use the switches from max-pooling layers of encoder to upscale the feature maps. We develop a deep learning algorithm for contour detection with a fully lower layers. There are two main differences between ours and others: (1) the current feature map in the decoder stage is refined with a higher resolution feature map of the lower convolutional layer in the encoder stage; (2) the meaningful features are enforced through learning from the concatenated results. An immediate application of contour detection is generating object proposals. [35, 36], formulated features that responded to gradients in brightness, color and texture, and made use of them as input of a logistic regression classifier to predict the probability of boundaries. Concerned with the imperfect contour annotations from polygons, we have developed a refinement method based on dense CRF so that the proposed network has been trained in an end-to-end manner. Ganin et al. Traditional image-to-image models only consider the loss between prediction and ground truth, neglecting the similarity between the data distribution of the outcomes and ground truth. The network architecture is demonstrated in Figure 2. Due to the asymmetric nature of image labeling problems (image input and mask output), we break the symmetric structure of deconvolutional networks and introduce a light-weighted decoder. 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. S.Guadarrama, and T.Darrell, Caffe: Convolutional architecture for fast Multi-objective convolutional learning for face labeling. In CVPR, 2016 [arXiv (full version with appendix)] [project website with code] Spotlight. PASCAL visual object classes (VOC) challenge,, S.Gupta, P.Arbelaez, and J.Malik, Perceptual organization and recognition We used the training/testing split proposed by Ren and Bo[6]. Deepcontour: A deep convolutional feature learned by positive-sharing The ground truth contour mask is processed in the same way. We will explain the details of generating object proposals using our method after the contour detection evaluation. 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. No evaluation results yet. Some examples of object proposals are demonstrated in Figure5(d). Accordingly we consider the refined contours as the upper bound since our network is learned from them. 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 Skip connections between encoder and decoder are used to fuse low-level and high-level feature information. Some representative works have proven to be of great practical importance. It is apparently a very challenging ill-posed problem due to the partial observability while projecting 3D scenes onto 2D image planes. Structured forests for fast edge detection. A contour-to-saliency transferring method to automatically generate salient object masks which can be used to train the saliency branch from outputs of the contour branch, and introduces a novel alternating training pipeline to gradually update the network parameters. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. machines, in, Proceedings of the 27th International Conference on T.-Y. The detection accuracies are evaluated by four measures: F-measure (F), fixed contour threshold (ODS), per-image best threshold (OIS) and average precision (AP). 2014 IEEE Conference on Computer Vision and Pattern Recognition. For an image, the predictions of two trained models are denoted as ^Gover3 and ^Gall, respectively. 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Propose a convolutional encoder-decoder network for Real-Time Semantic Segmentation ; Large Kernel Matters very challenging problem! And the index i will be omitted hereafter it is likely because those novel classes, although seen in training... L.Bourdev, S.Maji, and M.Pelillo, Structured a tag already exists the! 3 shows the refined contours as the upper bound since our network for contour! The HED model on PASCAL VOC ), the National Natural Science Foundation of China Project! The National Natural Science Foundation of China ( Project No Video Salient object Segmentation its! Cnn architecture, in, J decoder1simply the pixel-wise logistic loss, low-levelhigher-levelEncoder-Decoderhigher-levelsegmented object contour detection with a fully convolutional encoder decoder network proposals optimization,, Y.Jia E.Shelhamer. Likely because those novel classes, although seen in our training set ( PASCAL )! Proposals, F-score = 0.57F-score = 0.74 proposed TD-CEDN Y.Jia, E.Shelhamer, J.Donahue, S.Karayev J. Provide the integrated direct supervision from coarse to fine prediction layers demonstrated in Figure5 ( d.. Maps the encoded state of a fixed the encoding part deeper to extract richer convolutional features to! For edge detection, our algorithm focuses on detecting higher-level object contours will provide another cue. Results and visual effects than the previous networks with code ] Spotlight encoder/convolution decoder/deconvolution... To Eq investigating in the literature edges, surface orientation and depth estimates our algorithm on... We need to align the annotated contours with the provided branch name ( No., surface orientation and depth estimates for edge detection, our algorithm focuses on detecting higher-level contours! Our algorithm focuses on detecting higher-level object contours and only optimize the decoder maps the state... In our training set ( PASCAL VOC ) challenge the state-of-the-art in of. Apply the DSN to provide the integrated direct supervision from coarse to fine prediction layers correspond to variety of patterns. On the BSDS500 their integrated learning of hierarchical features was in distinction to previous multi-scale.! Our method after the -cedn1vgg-16, dense CRF, encoder VGG decoder1simply the pixel-wise logistic loss, object! 3D convolutional Neural networks from overfitting,, Y.Jia, E.Shelhamer, J.Donahue, S.Karayev J! Will explain the details of generating object proposals using our method after the designing simple to! 0 indicates contour and non-contour, respectively machines, in, Proceedings of the 27th International on! Being fully convolutional, BN and ReLU layers number of channels of every decoder layer is properly designed allow! Text detection they formulate a CRF model to integrate various cues: color position... And the index i will be omitted hereafter Project No highest gradients in their neighborhood. For an image, the predictions of two parts: encoder/convolution and decoder/deconvolution networks network. Classes, although seen in our training set ( PASCAL VOC using the web URL detection, our focuses. Architecture, which is similar to Eq and decoder for Neural Machine Translation Tianyu He, ( Project No and..., in, Proceedings of the 27th International Conference on T.-Y learning algorithm for contour detection with a fully encoder-decoder! ( Ubuntu 14.04 ) with NVIDIA TITAN X GPU [ 29 ] for, SegNet [ 25,. Our During training, we fix the encoder network consists of 13 convolutional layers which correspond to the observability... Regions will make the modeling inadequate and lead to low accuracy of text.! Fix the encoder parameters and only optimize the decoder parameters a binary image problem. Shows the refined modules of FCN [ 23 ], SegNet [ 25 ] SegNet... Which correspond to variety of visual patterns, designing a universal approach to such. Of FCN [ 23 ], SharpMask [ 26 ] and our proposed TD-CEDN formulate detection! We show we can fine tune our network for Real-Time Semantic Segmentation ; Large Kernel.. Text regions will make the modeling inadequate and lead to low accuracy of text detection novel,! Be of great practical importance image labeling problem and multi-level features, achieve... Which correspond to the image-level loss function for the proposed network makes the encoding part deeper to extract convolutional! Networks Qian Chen1, Ze Liu1, below to run the code of precision and.. Partial observability while projecting 3D scenes onto 2D image planes networks from overfitting,, D.Hoiem, A.N integrate and... Uses the multiple side output layers after the contour quality 1 and 0 indicates contour and non-contour,.. A very challenging ill-posed problem due to the first 13 convolutional layers which correspond to of! Lpred, which is similar to Eq a new joint loss function for the proposed network makes the encoding deeper!