Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. We show how to teach machines to paint like human painters, who can use a small number of strokes to create fantastic paintings. By employing a neural renderer in model-based Deep Reinforcement Learning DRLour agents learn to determine the position and color of each stroke and make long-term plans to decompose texture-rich images into strokes.

Experiments demonstrate that excellent visual effects can be achieved using hundreds of strokes. The training process does not require the experience of human painters or stroke tracking data.

You can easily use colaboratory to have a try. You can download a trained neural renderer and a CelebA actor for test: renderer.

We also provide with some other neural renderers and agents, you can use them instead of renderer. Also many thanks to ctmakro for inspiring this work. Skip to content. MIT License. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. SungYK acotr. Git stats 93 commits.

Failed to load latest commit information. View code. Dependencies PyTorch 1. Topics reinforcement-learning computer-vision deep-learning pytorch painting. Releases No releases published.Work fast with our official CLI.

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To better understand, we retrain our network and record some detailed training details as well as corresponding pre-trained models. Meanwhile, we also provide other state-of-the-art RGB-D methods' results on our proposed dataset, and you can directly download their results All results ,2gs2.

If you have any questions, please contact us wji3 ualberta.

Skip to content. MIT License. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 70 commits. Failed to load latest commit information. View code. The dataset will be expanded to about real scenes.

We are working on it and will make it publicly available soon. We create a large scale RGB-D dataset DUTLF-Depth with paired images containing more complex scenarios, such as multiple or transparent objects, similar foreground and background, complex background, low-intensity environment. This challenging dataset can contribute to comprehensively evaluating saliency models.

The dataset link can be found here. And we split the dataset including training set and test set. Meanwhile, you need to set dataset path and checkpoint name correctly. Because of the randomness of the training process, the results fluctuated slightly. The web link is here. Contact Us If you have any questions, please contact us wji3 ualberta.

Topics rgbd salient-object-detection saliency-detection.

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Releases No releases published. Packages 0 No packages published. Contributors 2. You signed in with another tab or window. Reload to refresh your session.Work fast with our official CLI.

iccv 2019 accepted papers github

Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

iccv 2019 accepted papers github

The datasets should simply be extracted and put in some folder. You need to adapt the "config. You can adjust the training parameters from config. Before running the default script, you must adjust config.

With default parameters in config. As of now September, 6th,the code supports 1. For CPU usage, it still works with recent Tensorflow versions tested with 1. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization (ICCV 2019)

MIT License. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 4 commits. Failed to load latest commit information. View code. Requirements Python version 3. Installation pip install -r requirements. Resources Readme.

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Releases No releases published. Packages 0 No packages published. You signed in with another tab or window.

Reload to refresh your session. You signed out in another tab or window.Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Thanks the contributions of their authors.

Please refer to install. Before training starts, to visualize the training results and the loss curve in real-time, please run python -m visdom.

To visualize the results, run. To run the model on your own images, just center crop the images according to each person. To run inference for the pre-processed images, please checkout to the inference branch first. Remember to commit the current changes you made in master branch first. To further visualize the results, run visualization code:. Skip to content. Go back. Launching Xcode If nothing happens, download Xcode and try again.

Latest commit. Git stats 11 commits. Failed to load latest commit information. Jan 29, Dec 14, Feb 7, View code. Prerequisites Install 3rd-party packages Please refer to install. Prepare DCT code Clone this repo git clone git github.

Releases No releases published. Packages 0 No packages published. You signed in with another tab or window.Work fast with our official CLI. Learn more.

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Describe an image using a limited numbers of tags, whereby the retrieved tags need to cover as much useful information about the image as possible. Also, it is surprising that many papers on Image Tagging has accepted this year. This repository is just made for my own studying, so there may be incorrect information. Also, I regard 'image tagging', 'image annotation', 'multi-label image classification' as same task actually may be little bit different in this repository.

I'd appreciate it if everybody could reccommend me image tagging paper that I can read. Thank you! Skip to content. A paper list of awesome Image Tagging 45 stars 7 forks. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 19 commits. Failed to load latest commit information. Jan 2, View code. Tags can be objects, situation, and user generated tags.

It means 'unseen at the training stage'. Email: kabbi gmail. About A paper list of awesome Image Tagging Topics deep-learning image-annotation multi-label-classification image-tagging multi-label-image-classification. Releases No releases published. Packages 0 No packages published.

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iccv 2019 accepted papers github

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Alternatively, you can use ensemble, logisticregression or deepnet arguments. Otherwise, BigML will predict the class with the higher confidence or probability (depending on the kind).

For non-boosted ensembles, there is a third kind available: votes. None of the fields in the dataset Specifies the fields to be included in the csv file. It can be a list of field ids or names. It will only have effect if header is true. Example: "Prediction" probabilities optional Boolean,default is false Whether to include the predicted class and all other possible class values for the batch prediction for the classification task. If enabled, the columns are included after the confidence score.

Example: true probability optional Boolean,default is false Whether the probability for each prediction for the classification task should be added.

It's 1 by default.

iccv 2019 accepted papers github

This is the usual default in some systems trying to detect anomalies (e. IDS and the like), and other uses of this combiner should probably not rely on our default value. Their use is deprecated, and maintained only for backwards compatibility. Example: true You can also use curl to customize a new batch prediction. For example, to create a new batch prediction named "my batch prediction", that will not include a header, and will only output the field "000001" together with the confidence for each prediction.

Once a batch prediction has been successfully created it will have the following properties. Creating a batch prediction is a process that can take just a few seconds or a few hours depending on the size of the dataset used as input and on the workload of BigML's systems.

The batch prediction goes through a number of states until its finished. Through the status field in the batch prediction you can determine when it has been fully processed. Once you delete a batch prediction, it is permanently deleted. If you try to delete a batch prediction a second time, or a batch prediction that does not exist, you will receive a "404 not found" response. However, if you try to delete a batch prediction that is being used at the moment, then BigML.

To list all the batch predictions, you can use the batchprediction base URL. By default, only the 20 most recent batch predictions will be returned.

You can get your list of batch predictions directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your batch predictions. Batch Centroids Last Updated: Monday, 2017-10-30 10:31 A batch centroid provides an easy way to compute a centroid for each instance in a dataset in only one request. Batch centroids are created asynchronously. You can also list all of your batch centroids.

You can easily create a new batch centroid using curl as follows.