Review — DSepConv: Video Frame Interpolation via Deformable Separable Convolution (Video Frame…



Original Source Here

Review — DSepConv: Video Frame Interpolation via Deformable Separable Convolution (Video Frame Interpolation)

Left: SepConv, bad interpolation for the ball, Right: Proposed DSepConv, considers more relevant pixels far away from local grid (black rectangle) with a much smaller kernel size and performs better

In this story, Video Frame Interpolation via Deformable Separable Convolution, (DSepConv), by Wuhan University, is reviewed.

Conventionally, when scene motion is larger than the predefined kernel size, kernel based methods yield poor results.

In this paper:

  • Deformable separable convolution (DSepConv) is used to adaptively estimate kernels, offsets and masks to allow the network to obtain information with much fewer but more relevant pixels.

This is a paper in 2020 AAAI. (Sik-Ho Tsang @ Medium)

AI/ML

Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot



via WordPress https://ramseyelbasheer.io/2021/05/16/review%e2%80%8a-%e2%80%8adsepconv-video-frame-interpolation-via-deformable-separable-convolution-video-frame/

Popular posts from this blog

Fully Explained DBScan Clustering Algorithm with Python

Image
Original Source Here Fully Explained DBScan Clustering Algorithm with Python Unsupervised learning in machine learning on density-based clusters Clustering In unsupervised Learning. A photo by Author In this article, we will discuss the machine learning clustering-based algorithm that is the DBScan cluster. The approach in this cluster algorithm is density-based than another distance-based approach. The other cluster which is distance-based looks for closeness in data points but also misclassifies if the point belongs to another class. So, density-based clustering is suited in this kind of scenario. The cluster algorithms come in unsupervised learning in which we don’t rely on target variables to make clusters. In a cluster, the main concern is on maximum dense connected points. The density-based clustering can be useful in arbitrary shapes even with noise. The working function of the density-based cluster is shown below: We select a random data point. Then it will ...
Read more

Streamlit — Deploy your app in just a few minutes

Image
Original Source Here Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data… Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/07/11/streamlit%e2%80%8a-%e2%80%8adeploy-your-app-in-just-a-few-minutes/
Read more

Hierarchical clustering explained

Image
Original Source Here Interpretation of dendrogram Each level of dendrogram has a subtle meaning to the relationship between its data members. In a regular relationship chart, one may interpret that at the top lies grandparents or the first generation, the next level corresponds to parents or second generation and the final level belongs to children or third generation. Likewise, in every branching procedure of dendrogram, all the data points having the membership at each level belongs to a certain class. However, to infer this class entity, one has to go through a few individual samples of each level within the formulated cluster and find out what feature is common in the resulting cluster. Also, these inferred classes need not be similar at the sister branches. For example, in level 2, the cats have been clustered on big ears and calm behavior but dogs have been clustered on a similar attribute of size. Inferred classes of data in dendrogram by the user (Image by Author) C...
Read more