Torr 4. Recent progress on salient object detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks CNNs.

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Semantic segmentation and salient object detection algorithms developed lately have been mostly based on Fully Convolutional Neural Networks FCNs. There is still a large room for improvement over the generic FCN models that do not explicitly deal with the scale-space problem. Holistically-Nested Edge Detector HED provides a skip-layer structure with deep supervision for edge and boundary detection, but the performance gain of HED on saliency detection is not obvious.

In this paper, we propose a new salient object detection method by introducing short connections to the skip-layer structures within the HED architecture. Our framework takes full advantage of multi-level and multi-scale features extracted from FCNs, providing more advanced representations at each layer, a property that is critically needed to perform segment detection.

Our method produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency 0. Beyond that, we conduct an exhaustive analysis on the role of training data on performance.

Our experimental results provide a more reasonable and powerful training set for future research and fair comparisons. We have uploaded the caffe and CRF packages we used in our paper. To eliminate the divergence of models training on different dataset, we encourage researcher to use the large dataset as shown in our PAMI version. A report in Nature: link. Does that mean that you use the original training images and set the batch size to 10?

I found that in the training dataset MSRA-B, some images have different sizes, so how do you use the different size of images in a batch? In addition, can you plz give the detail about the learning rate ,decay parameters and step size? Thank you so much! Do you mean that we should train the model with an initial learning rate of 0.

The basic learning rate specified in the paper as well as in the open sourced code is 1e When we tried to train the model with a learning rate of 1e-8 using Momentum optimizer, it seemed that the side-output layers could not learn feartures in a right way.

Some of the side-output layers would always output images that were completely white inspite of different input images. What do you think may cause such a phenomenon? Thanks for your time. This means if your total loss is divided by the number of pixels then you can set lr to 1e If not, you need to set it to 1e-8 or less. By the way, is it necessary to set different learnig rates for the backbone network layers and the side-output layers?

I follow your paper to replace the basic model VGG by ResNetbut I cannot get your results reported in your paper on some datasets, the results are even worse than the VGG.

Hi, Qibin. If you know a method to solve the problem, please to help me. Thank you very much!!! Thank you! Please tell us how to get these files. Thank you very much. Skip to content Research. Connect with. Connect with:. Most reacted comment. Hottest comment thread. Recent comment authors.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here.

Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Can someone explain the Mask Loss function. How the use FCN to improve?

图像分割领域常见的loss fuction有哪一些?

FCN uses per-pixel softmax and a multinominal loss. This means, that the mask prediction task the boundaries of the object and the class prediction task what is the object being masked are coupled.

Mask-RCNN decouples these tasks: the existing bounding-box prediction AKA the localization task head predicts the class, like faster-RCNN, and the mask branch generates a mask for each classwithout competition among classes e. Soft-max in FCN. See table 2. The mask branch generates a mask of dimension m x m for each RoI and each class; K classes in total.

Because the model is trying to learn a mask for each class, there is no competition among classes for generating masks. Learn more. Ask Question. Asked 2 years, 7 months ago. Active 3 months ago. Viewed 6k times. Shamane Siriwardhana Shamane Siriwardhana 2, 3 3 gold badges 19 19 silver badges 58 58 bronze badges. Active Oldest Votes. I am a bit confused. The second output was the bounding box regression. You said, 21 claess predicts 21 masks, then inside one class how to distinguish instances, I mean, if one mask have a class all instances, how to divide them out?

Lmask : is defined as the average binary cross-entropy loss, only including k-th mask if the region is associated with the ground truth class k. Saleem Ahmed Saleem Ahmed 1, 1 1 gold badge 12 12 silver badges 25 25 bronze badges. Sign up or log in Sign up using Google.

Sign up using Facebook. Sign up using Email and Password. Post as a guest Name.Bounding box regression is the crucial step in object detection. Recently, IoU loss and generalized IoU GIoU loss have been proposed to benefit the IoU metric, but still suffer from the problems of slow convergence and inaccurate regression.

In this paper, we propose a Distance-IoU DIoU loss by incorporating the normalized distance between the predicted box and the target box, which converges much faster in training than IoU and GIoU losses.

Furthermore, this paper summarizes three geometric factors in bounding box regression,overlap area, central point distance and aspect ratio, based on which a Complete IoU CIoU loss is proposed, thereby leading to faster convergence and better performance. Moreover, DIoU can be easily adopted into non-maximum suppression NMS to act as the criterion, further boosting performance improvement. Zhaohui Zheng. Ping Wang. Wei Liu. Jinze Li. Rongguang Ye.

Dongwei Ren. Intersection over Union IoU is the most popular evaluation metric used Popular rotated detection methods usually use five parameters coordinat Current object detection frameworks mainly rely on bounding box regressi We demonstrate that many detection methods are designed to identify only Most state of the art object detectors output multiple detections per ob In this paper we propose an approach for monocular 3D object detection f Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

In this section, we briefly survey relevant works including object detection methods, loss function for bounding box regression and non-maximum suppression. In [ 24 ]the central axis line is applied in pedestrian detection. CornerNet [ 12 ] suggested predicting a pair of corners to replace a rectangular box for locating object.

损失函数:L1 loss, L2 loss, smooth L1 loss

In RepPoints [ 29 ]a rectangular box is formed by predicting several points. Recently, FSAF [ 31 ] proposed anchor-free branch to tackle the issues of non-optimality in online feature selection. There are also several loss functions for object detection, e.

soft iou loss

IoU loss is also used since Unitbox [ 30 ]which is invariant to the scale. GIoU [ 23 ] loss is proposed to tackle the issues of gradient vanishing for non-overlapping cases, but is still facing the problems of slow convergence and inaccurate regression. NMS is the last step in most object detection algorithms, in which redundant detection boxes are removed as long as its overlap with the highest score box exceeds a threshold.

Soft-NMS [ 2 ] penalizes the detection score of neighbors by a continuous function w. IoU, yielding softer and more robust suppression than original NMS.

Recently, adaptive NMS [ 16 ] and Softer-NMS [ 10 ] are proposed to respectively study proper threshold and weighted average strategies. In this work, DIoU is simply deployed as the criterion in original NMS, in which the overlap area and the distance between two central points of bounding boxes are simultaneously considered when suppressing redundant boxes. However, it is very difficult to analyze the procedure of bounding box regression simply from the detection results, where the regression cases in uncontrolled benchmarks are often not comprehensive, e.

Instead, we suggest conducting simulation experiments, where the regression cases should be comprehensively considered, and then the issues of a given loss function can be easily analyzed. In the simulation experiments, we try to cover most of the relationships between bounding boxes in terms of distance, scale and aspect ratio, as shown in Fig.The Jaccard coefficient measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets:.

The Jaccard distancewhich measures dis similarity between sample sets, is complementary to the Jaccard coefficient and is obtained by subtracting the Jaccard coefficient from 1, or, equivalently, by dividing the difference of the sizes of the union and the intersection of two sets by the size of the union:. This distance is a metric on the collection of all finite sets.

There is also a version of the Jaccard distance for measuresincluding probability measures. The MinHash min-wise independent permutations locality sensitive hashing scheme may be used to efficiently compute an accurate estimate of the Jaccard similarity coefficient of pairs of sets, where each set is represented by a constant-sized signature derived from the minimum values of a hash function. Given two objects, A and Beach with n binary attributes, the Jaccard coefficient is a useful measure of the overlap that A and B share with their attributes.

Each attribute of A and B can either be 0 or 1. The total number of each combination of attributes for both A and B are specified as follows:. When used for binary attributes, the Jaccard index is very similar to the simple matching coefficient. Thus, the SMC counts both mutual presences when an attribute is present in both sets and mutual absence when an attribute is absent in both sets as matches and compares it to the total number of attributes in the universe, whereas the Jaccard index only counts mutual presence as matches and compares it to the number of attributes that have been chosen by at least one of the two sets.

In market basket analysisfor example, the basket of two consumers who we wish to compare might only contain a small fraction of all the available products in the store, so the SMC will usually return very high values of similarities even when the baskets bear very little resemblance, thus making the Jaccard index a more appropriate measure of similarity in that context. For example, consider a supermarket with products and two customers.

The basket of the first customer contains salt and pepper and the basket of the second contains salt and sugar.

soft iou loss

In other contexts, where 0 and 1 carry equivalent information symmetrythe SMC is a better measure of similarity. For example, vectors of demographic variables stored in dummy variablessuch as gender, would be better compared with the SMC than with the Jaccard index since the impact of gender on similarity should be equal, independently of whether male is defined as a 0 and female as a 1 or the other way around.

However, when we have symmetric dummy variables, one could replicate the behaviour of the SMC by splitting the dummies into two binary attributes in this case, male and femalethus transforming them into asymmetric attributes, allowing the use of the Jaccard index without introducing any bias. The SMC remains, however, more computationally efficient in the case of symmetric dummy variables since it does not require adding extra dimensions.

TensorFlow, Deep Learning, and Modern Convolutional Neural Nets, Without a PhD (Cloud Next '18)

Then Jaccard distance is. The weighted Jaccard similarity described above generalizes the Jaccard Index to positive vectors, where a set corresponds to a binary vector given by the indicator functioni. However, it does not generalize the Jaccard Index to probability distributions, where a set corresponds to a uniform probability distribution, i.

It is always less if the sets differ in size. Instead, a generalization that is continuous between probability distributions and their corresponding support sets is.Louis, 1 Brookings Dr.

While our previous FlyNet 1. To train and test FlyNet 2. This increased segmentation accuracy allows morphological and dynamic cardiac parameters to be better quantified. Drosophila melanogaster, widely known as the fruit fly, shows many similarities with vertebrates in the early stages of heart development [ 1 ].

As a powerful genetic model, Drosophila has been used to investigate heart development and cardiac diseases [ 5 — 9 ]. Recently, Drosophila was also used to develop a novel optogenetic pacing technique that can noninvasively control the heart rhythm [ 10 ].

Optical coherence tomography OCT [ 11 — 14 ] is an emerging biomedical imaging technology that enables noninvasive micron-scale, cross-sectional, 3D imaging of biological tissues. OCT is widely used for clinical applications, including ophthalmology [ 15 — 17 ], cardiology [ 18 ], endoscopy [ 19 ], dermatology [ 2021 ], and dentistry [ 22 ].

In this project, the OCM system acquires cross-sectional videos of the fruit fly heart in vivoeach containing either 4, or 6, OCM cross-sectional images that cover over a hundred heartbeat cycles. With accurate fruit fly heart segmentation from 2D OCM images, dynamic cardiac parameters, such as fly heart area, heart rate, end-diastolic diameter EDDend systolic diameter ESDand fraction shortening FScan be accurately measured.

However, due to the large data size of the Drosophila cardiac OCM recording, a robust and fast method to segment the fruit fly heart is needed. Different methods have been utilized for object detection and segmentation over the last several decades.

These include traditional methods, like the histogram of oriented gradients HOG [ 2324 ], the scale-invariant feature transform SIFT [ 25 ], and the features from accelerated segment test FAST [ 26 ]. Thresholding methods have also been widely used for grayscale image segmentation [ 27 — 29 ]. In addition, k-means [ 7 ] and support vector machines SVMs [ 30 ] have been employed to segment images. However, high segmentation accuracy and universality are difficult to achieve with these traditional methods.

The success of deep neural networks DNN in various computer vision tasks has inspired researchers to apply DNN in image segmentation, and many network structures have been designed for this application. It employs a convolutional neural network CNN [ 32 ] to extract image feature maps and achieves a significant improvement in training accuracy compared to traditional methods.

Later, more advanced convolutional networks were designed to improve performance further.

soft iou loss

The SegNet [ 33 ] and U-Net [ 34 ] networks added a decoder stage, which is a symmetrically expanding path, to accurately localize features. With a much better model structure, DNN has much-improved segmentation accuracy over traditional methods.

As a starting point, we employed U-Net based convolutional neural networks to segment fly heart OCM videos and called the software FlyNet 1. Traditional CNNs extract only 2D spatial information from each OCM image, but fruit fly heartbeat videos contain time sequence information between adjacent images which can be used to improve the segmentation performance further.

Long short-term memory LSTM [ 36 ] is an artificial recurrent neural network architecture that has feedback connections between time sequences. LSTM networks have internal contextual state cells that act as long-term or short-term memory cells. This property allows the historical state of the input image to affect the segmentation prediction.

In this project, instead of segmenting the fly heart only from 2D OCM images, we further utilized the time-dependent information contained in the fly heart OCM videos. Employing the convolutional LSTM network found correlations between single fly heart OCM images separated by many frames, and these correlations can help us extract time-dependent information features of the fly heartbeat. With this well-trained FlyNet 2. We acquired fruit fly heartbeat videos with the custom OCM system [ 10 ] shown in Fig.

A rod mirror splits the laser beam into sample and reference arms. We collected OCM images from different flies in three different developmental stages larva, early pupa, and adult. The datasets include 35 larva, 35 early pupa, and 30 adult flies. The fly heartbeat videos for different developmental stages were equally distributed for training, validating, and testing dataset.

Table 1 shows how datasets were distributed for training, validating, and testing with different developmental stages.It's very similar but it provides a loss gradient even near 0, leading to better accuracy. I'm sorry to answer to such an old topic but I have to correct you here diegovincent. There is no need to substract the intersection. Hi wassname can you please explain why you're taking sum of the squares in the denominator?

By that I mean this. The other implementations don't take the sum of squares, instead only the sum. Please explain. Why the difference? Those vertical bars around the numbers are vector norm. It's possible that the implementations you are comparing use different vector norms, I went with the one in the paper since it had demonstrated performance.

The paper is also listing the equation for dice loss, not the dice equation so it may be the whole thing is squared for greater stability. I guess you will have to dig deeper for the answer. Skip to content. Instantly share code, notes, and snippets. Code Revisions 1 Stars 27 Forks 1. Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs.

Download ZIP. This comment has been minimized. Sign in to view. Copy link Quote reply. That is, line 17 should be: return 2. Thanks :. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. You signed in with another tab or window.

Reload to refresh your session. You signed out in another tab or window. Here is a dice loss for keras which is smoothed to approximate a linear L1 loss.Going on a crash or mono diet is not the best solution for weight loss. By having the right kinds of food, you can shed those extra pounds in the body. In order to lose weight, you need to eat foods that are low in calories as well as fats. There are many fat burning foods that can help you lose weight and get into shape.

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Rich in water and low in calories, this juicy vegetable can be the best ingredient for a dieter's salad meal. The green leafy superfood must be included in a weight loss diet. Spinach has numerous health benefits.

It is low in calories which makes it a perfect vegetable for weight loss. It contains fiber and water that curb hunger pangs. It is best to start the day with the fiber rich bottle gourd juice. Do not strain the bottle gourd juice as the fiber gets separated from juice when sieved.

soft iou loss

Fiber helps in bringing down calories. This is another green vegetable that can help in weight loss. Have boiled or steamed broccoli as these forms of cooking keeps the nutritional and health benefits of the vegetable intact. The vegetable is rich in essential vitamins and fiber which are required for weight loss.

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This green leafy vegetable is rich in fiber and water, and low in calories which fills the stomach and aids weight loss. They look like cucumber, but the green vegetable is one of the best sources of nutritional supplements. It aids weight loss and also acts as a medicine for health problems like urinary tract infection.

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Celery is filling and also burns fat deposits in the body.

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