object segmentation and fine-grained localization, in, W.Liu, A.Rabinovich, and A.C. Berg, ParseNet: Looking wider to see The network architecture is demonstrated in Figure 2. Indoor segmentation and support inference from rgbd images. The proposed network makes the encoding part deeper to extract richer convolutional features. z-mousavi/ContourGraphCut Task~2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy. [13] has cleaned up the dataset and applied it to evaluate the performances of object contour detection. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network . 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. [46] generated a global interpretation of an image in term of a small set of salient smooth curves. Moreover, we will try to apply our method for some applications, such as generating proposals and instance segmentation. View 6 excerpts, references methods and background. 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. Different from the original network, we apply the BN[28] layer to reduce the internal covariate shift between each convolutional layer and the ReLU[29] layer. Highlights We design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. Recovering occlusion boundaries from a single image. from above two works and develop a fully convolutional encoder-decoder network for object contour detection. inaccurate polygon annotations, yielding much higher precision in object We report the AR and ABO results in Figure11. T.-Y. Each side-output can produce a loss termed Lside. Quantitatively, we evaluate both the pretrained and fine-tuned models on the test set in comparisons with previous methods. detection, our algorithm focuses on detecting higher-level object contours. PASCAL visual object classes (VOC) challenge,, S.Gupta, P.Arbelaez, and J.Malik, Perceptual organization and recognition We choose this dataset for training our object contour detector with the proposed fully convolutional encoder-decoder network. 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Continue Reading. Long, R.Girshick, We train the network using Caffe[23]. Edge boxes: Locating object proposals from edge. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Arbelaez et al. RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation, and achieves state-of-the-art performance on several available datasets. Note that we did not train CEDN on MS COCO. To automate the operation-level monitoring of construction and built environments, there have been much effort to develop computer vision technologies. It is composed of 200 training, 100 validation and 200 testing images. connected crfs. We used the training/testing split proposed by Ren and Bo[6]. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. Given image-contour pairs, we formulate object contour detection as an image labeling problem. BN and ReLU represent the batch normalization and the activation function, respectively. A.Karpathy, A.Khosla, M.Bernstein, N.Srivastava, G.E. Hinton, A.Krizhevsky, I.Sutskever, and R.Salakhutdinov, D.Hoiem, A.N. Stein, A.Efros, and M.Hebert. 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. lower layers. Ren et al. 9 presents our fused results and the CEDN published predictions. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). booktitle = "Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016", Object contour detection with a fully convolutional encoder-decoder network, Chapter in Book/Report/Conference proceeding, 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. Some other methods[45, 46, 47] tried to solve this issue with different strategies. machines, in, Proceedings of the 27th International Conference on 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. The same measurements applied on the BSDS500 dataset were evaluated. S.Guadarrama, and T.Darrell. Semi-Supervised Video Salient Object Detection Using Pseudo-Labels; Contour Loss: Boundary-Aware Learning for Salient Object Segmentation . Their integrated learning of hierarchical features was in distinction to previous multi-scale approaches. Use Git or checkout with SVN using the web URL. 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. The number of channels of every decoder layer is properly designed to allow unpooling from its corresponding max-pooling layer. We present results in the MS COCO 2014 validation set, shortly COCO val2014 that includes 40504 images annotated by polygons from 80 object classes. P.Rantalankila, J.Kannala, and E.Rahtu. Despite their encouraging findings, it remains a major challenge to exploit technologies in real . We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. We find that the learned model . persons; conferences; journals; series; search. 2013 IEEE International Conference on Computer Vision. N1 - Funding Information: A fully convolutional encoder-decoder network is proposed to detect the general object contours [10]. semantic segmentation, in, H.Noh, S.Hong, and B.Han, Learning deconvolution network for semantic To address the problem of irregular text regions in natural scenes, we propose an arbitrary-shaped text detection model based on Deformable DETR called BSNet. By clicking accept or continuing to use the site, you agree to the terms outlined in our. a Fully Fourier Space Spherical Convolutional Neural Network Risi Kondor, Zhen Lin, . It employs the use of attention gates (AG) that focus on target structures, while suppressing . 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.. 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. Our Therefore, the traditional cross-entropy loss function is redesigned as follows: where refers to a class-balancing weight, and I(k) and G(k) denote the values of the k-th pixel in I and G, respectively. / Yang, Jimei; Price, Brian; Cohen, Scott et al. Accordingly we consider the refined contours as the upper bound since our network is learned from them. V.Ferrari, L.Fevrier, F.Jurie, and C.Schmid. 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. Recently deep convolutional networks[29] have demonstrated remarkable ability of learning high-level representations for object recognition[18, 10]. Note that we fix the training patch to. The oriented energy methods[32, 33], tried to obtain a richer description via using a family of quadrature pairs of even and odd symmetric filters. BDSD500[14] is a standard benchmark for contour detection. and find the network generalizes well to objects in similar super-categories to those in the training set, e.g. We have combined the proposed contour detector with multiscale combinatorial grouping algorithm for generating segmented object proposals, which significantly advances the state-of-the-art on PASCAL VOC. 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). HED integrated FCN[23] and DSN[30] to learn meaningful features from multiple level layers in a single trimmed VGG-16 net. Since we convert the fc6 to be convolutional, so we name it conv6 in our decoder. generalizes well to unseen object classes from the same super-categories on MS In the encoder part, all of the pooling layers are max-pooling with a 2, (d) The used refined module for our proposed TD-CEDN, P.Arbelaez, M.Maire, C.Fowlkes, and J.Malik, Contour detection and 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]. We first present results on the PASCAL VOC 2012 validation set, shortly PASCAL val2012, with comparisons to three baselines, structured edge detection (SE)[12], singlescale combinatorial grouping (SCG) and multiscale combinatorial grouping (MCG)[4]. 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]. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. 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. inaccurate polygon annotations, yielding much higher precision in object 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. SegNet[25] used the max pooling indices to upsample (without learning) the feature maps and convolved with a trainable decoder network. Wu et al. Very deep convolutional networks for large-scale image recognition. You signed in with another tab or window. The dataset is mainly used for indoor scene segmentation, which is similar to PASCAL VOC 2012 but provides the depth map for each image. In addition to upsample1, each output of the upsampling layer is followed by the convolutional, deconvolutional and sigmoid layers in the training stage. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 2015BAA027), the National Natural Science Foundation of China (Project No. We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. For simplicity, we set as a constant value of 0.5. potentials. 2016 IEEE. 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. A tag already exists with the provided branch name. [57], we can get 10528 and 1449 images for training and validation. The detection accuracies are evaluated by four measures: F-measure (F), fixed contour threshold (ODS), per-image best threshold (OIS) and average precision (AP). . Fig. 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]. Convolutional Oriented Boundaries gives a significant leap in performance over the state-of-the-art, and generalizes very well to unseen categories and datasets, and learning to estimate not only contour strength but also orientation provides more accurate results. Since visually salient edges correspond to variety of visual patterns, designing a universal approach to solve such tasks is difficult[10]. Several example results are listed in Fig. training by reducing internal covariate shift,, C.-Y. 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. Different from previous low-level edge detection, our algorithm focuses on detecting higher-level object contours. Rich feature hierarchies for accurate object detection and semantic 520 - 527. The decoder maps the encoded state of a fixed . This work was partially supported by the National Natural Science Foundation of China (Project No. To find the high-fidelity contour ground truth for training, we need to align the annotated contours with the true image boundaries. The dataset is divided into three parts: 200 for training, 100 for validation and the rest 200 for test. object detection. 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. J.J. Kivinen, C.K. Williams, and N.Heess. Conference on Computer Vision and Pattern Recognition (CVPR), V.Nair and G.E. Hinton, Rectified linear units improve restricted boltzmann In CVPR, 2016 [arXiv (full version with appendix)] [project website with code] Spotlight. Object Contour Detection with a Fully Convolutional Encoder-Decoder Network. This work claims that recognizing objects and predicting contours are two mutually related tasks, and shows that it can invert the commonly established pipeline: instead of detecting contours with low-level cues for a higher-level recognition task, it exploits object-related features as high- level cues for contour detection. Papers With Code is a free resource with all data licensed under. The final high dimensional features of the output of the decoder are fed to a trainable convolutional layer with a kernel size of 1 and an output channel of 1, and then the reduced feature map is applied to a sigmoid layer to generate a soft prediction. regions. to 0.67) with a relatively small amount of candidates ($\sim$1660 per image). 41571436), the Hubei Province Science and Technology Support Program, China (Project No. DeepLabv3. nets, in, J. 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. Compared the HED-RGB with the TD-CEDN-RGB (ours), it shows a same indication that our method can predict the contours more precisely and clearly, though its published F-scores (the F-score of 0.720 for RGB and the F-score of 0.746 for RGBD) are higher than ours. Lin, M.Maire, S.Belongie, J.Hays, P.Perona, D.Ramanan, This study proposes an end-to-end encoder-decoder multi-tasking CNN for joint blood accumulation detection and tool segmentation in laparoscopic surgery to maintain the operating room as clean as possible and, consequently, improve the . 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