saliency | Contextual Encoder-Decoder Network for Visual Saliency Prediction [Neural Networks 2020] | Machine Learning library
kandi X-RAY | saliency Summary
kandi X-RAY | saliency Summary
Contextual Encoder-Decoder Network for Visual Saliency Prediction [Neural Networks 2020]
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- Train the model
- Loads test set
- Fetch a dataset from files
- Returns the iterator for the dataset
- Loads the model
- Crop the image
- Postprocess the saliency map
- Load the training data
- Ensures that all zipped files are consistent
- Return random indices
- Define paths
- Loads training data
- Loads the training data
- Loads the dataset
saliency Key Features
saliency Examples and Code Snippets
Community Discussions
Trending Discussions on saliency
QUESTION
I try to build the OpenCV 4.5.1 SDK for Android because I need the SDK with contrib modules and the official release [1] only has the standard modules. But the libraries I build are almost 10x larger, for example: libopencv_core.a ==> 47.6 MB self compiled, 5.3 MB from the official repository (both for arm64-v8a)
Here is my cmake command, followed by ninja for compilation.
...ANSWER
Answered 2021-Mar-23 at 12:28I found the culprit:
QUESTION
I have a list of values ranging from around -0.15 to 0.08:
...ANSWER
Answered 2021-Jan-31 at 20:49When a token has multiple saliencies, seaborn's barplot takes the average of these saliencies. So, not the original saliencies are shown, but the averages per token, together with an error bar.
To color them, you can first create the barplot, and in a second pass loop through the created bars and assign them a color depending on their width. (Note that in the latest matplotlib versions DivergingNorm has been renamed to TwoSlopeNorm.)
QUESTION
I am trying to compile OpenCV for Android with contrib modules, mainly I am interested in sfm. I did a lot of research and finaly I did the following in order to support sfm:
Compiled gflags Compiled Glog Compiled Ceres
After that I used this cmake command to build and generate (partial output is given below):
...ANSWER
Answered 2021-Jan-24 at 21:16I just finished build opencv with android using this :
for ceres
QUESTION
I'm trying to build an LSTM network to classify sentences and provide explanation for the classification using saliency. This network must learn from the true class y_true as well as from which words he shouldn't pay attention Z (binary mask).
This paper inspired us to come up with our loss function. Here's what I'd like my loss function to look like :
Coût de classification translates to classification_loss and Coût d'explication (saillance) to saliency_loss (which is the same as gradient of output wrt the input) in the code below. I tried to implement this with a custom Model in Keras, with Tensorflow as backend :
ANSWER
Answered 2020-Dec-13 at 17:31Try to decorate train_step() with @tf.function
QUESTION
OpenCV 4.5 - Ubuntu - Jetson Nano 2GB
Hello I have a problem with getting video from my webcam (connected by USB to JetsonNano) by OpenCV
Here's my code:
...ANSWER
Answered 2020-Nov-27 at 06:41For points 1 and 2, you can test by passing the second argument to VideoCapture(filename[, apiPreference]). Since you have OpenCV built with ffmpeg and v4l2. I would try the following options cv::CAP_FFMPEG, and CAP_V4L2. For point 4, make sure your webcam is 1080p.
QUESTION
I'm working on project which detects people. So I'm using saliency in opencv and applying k-means clustering on the output of the saliency.
The problem is the output after applying k-means clustering is totally black
Here is the code:
...ANSWER
Answered 2020-Sep-17 at 15:35The key is converting the format into uint8 and scaling the intensities by 255 after you create the map. You did that for the first type of saliency map but not the second:
QUESTION
OpenCV Version: 4.4.0 (latest) OpenVINO Version: 2020.4 (latest)
The error happens when trying to build the project which is generated by CMake 3.18.0 on Windows 10. I have tried many times building from scratch (clear all caches, update the source code, reinstall the OpenVINO toolkit, and run it's all demos successfully), but the problem still exists.
Here is the VS 2019 build error logs:
...ANSWER
Answered 2020-Sep-02 at 13:44The problem was solved by configuring CMake with ngraph flag and locating its CMake files DIR location.
QUESTION
I am trying to construct a basic "vanilla gradient" saliency heatmap (gradient-based feature attribution) for MNIST using keras. I know there are libraries such as this one to compute saliency heatmaps, but I would like to construct this from scratch since the vanilla gradient approach seems conceptually straightforward to implement. I have trained the following digit classifier in Keras using functional model definition:
...ANSWER
Answered 2020-Aug-11 at 14:37If you add the activation as a single layer after the last dense layer with:
keras.layers.Activation('softmax')
you can do:
linear_model = keras.Model(input=model, output=model.layers[-2].output)
To then compute the gradients like:
QUESTION
I am trying to install latest OpenCV from here: https://github.com/opencv/opencv on my Ubuntu 20.04.
I need python and java support. I have installed a lot of per-requisites and used this cmake command to configure the build using the command line:
...ANSWER
Answered 2020-Jul-07 at 07:35Meanwhile I found the reason. The problem was this option: -DBUILD_opencv_world=ON When I removed it:
QUESTION
I am developing a SLAM algorithm in C, and I have implemented the FAST corner finding method which gives me some strong keypoints in the image. The next step is to get the center of the keypoints with a sub-pixel accuracy, therefore I extract a 3x3 patch around each of them, and do a Least Squares fit of a two dimensional quadratic:
Where f(x,y) is the corner saliency measure of each pixel, similar to the FAST score proposed on the original paper, but modified to also provide a saliency measure in non corner pixels.
And the least squares:
With being the estimated parameters.
I can now calculate the location of the peak of the fitted quadratic, by taking the gradient equal to zero, achieving my original goal.
The issue arises on some corner cases, where the local peak is closer to the edge of the window, resulting in a fit with low residuals but a peak of the quadratic way outside the window.
An example:
The corner saliency and a contour of the fitted quadratic:
The saliency (blue) and fit (red) as 3D meshes:
Numeric values of this example are (row-major ordering):
...ANSWER
Answered 2020-Apr-15 at 18:37The obvious answer is to reject 3x3 (or 5x5, whatever you use) boxes whose discrete maximum is not at the center. In other words, to use a quadratic approximation only to refine the location of a maximum that must be located inside the box.
More generally, in such cases the first questions to ask is not "How do I constrain my model-fitting procedure to shoehorn a solution for this edge case?", but rather "Does my model apply to this edge case?" and "Is this edge case even worth spending time on, or can I just ignore it?"
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install saliency
You can use saliency like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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