254 skills found · Page 1 of 9
VITA-Group / DeblurGANv2[ICCV 2019] "DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better" by Orest Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang
gvinciguerra / PGM Index🏅State-of-the-art learned data structure that enables fast lookup, predecessor, range searches and updates in arrays of billions of items using orders of magnitude less space than traditional indexes
JusperLee / Conv TasNetConv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation Pytorch's Implement
yxlu-0102 / MP SENetExplicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement
decile-team / CordsReduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order of magnitude using coresets and data selection.
tianyu0207 / RTFMOfficial code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021]
dhvanikotak / Emotion Detection In VideosThe aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
Zehong-Ma / ComfyUI MagCacheThe official code that integrates MagCache (Fast Video Generation with Magnitude-Aware Cache) with ComfyUI.
Zehong-Ma / MagCacheThe official code for NeurIPS 2025 "MagCache: Fast Video Generation with Magnitude-Aware Cache"
Patashu / Break Infinity.jsA replacement for decimal.js for incremental games who want to deal with very large numbers (bigger in magnitude than 1e308, up to as much as 1e(9e15) ) and want to prioritize speed over accuracy.
Utkarsh-Deshmukh / Spatially Varying Blur Detection Pythonpython implementation of the paper "Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes" - cvpr 2017
bkvogel / Griffin LimImplementation of the Griffin and Lim algorithm to recover an audio signal from a magnitude-only spectrogram.
phamquiluan / JdeskewICIP 2022: Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation
zzd1992 / Image Local AttentionA better PyTorch implementation of image local attention which reduces the GPU memory by an order of magnitude.
ehab-abdelhamid / GraMiGraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Also, GraMi supports user-defined structural and semantic constraints over the results, as well as approximate results. For more details, check our paper: Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. PVLDB, 7(7):517-528, 2014.
breadthe / SeismicA taskbar app for displaying USGS magnitude 2.5+ earthquakes from the past day.
dream-faster / Fold🪁 A fast Adaptive Machine Learning library for Time-Series, that lets you build, deploy and update composite models easily. An order of magnitude speed-up, combined with flexibility and rigour. This is an internal project - documentation is not updated anymore and substantially differ from the current API.
lmizrahi / Etascalibrate ETAS, simulate using ETAS, estimate completeness magnitude & magnitude frequency distribution
K2 / ADMMutateClassic code from 1999+ I am fairly sure this is the first public polymorphic shellcode ever (best IMHO and others http://ids.cs.columbia.edu/sites/default/files/ccs07poly.pdf :) If I ever port this to 64 or implement a few other suggestions (sorry I lost ppc code version contributed) it will be orders of magnitude more difficult to spot, so I hope nobody uses signatures for anything (virus / malware scanners included).
Daniel-Liu-c0deb0t / UMICollapseAccelerating the deduplication and collapsing process for reads with Unique Molecular Identifiers (UMI). Heavily optimized for scalability and orders of magnitude faster than a previous tool.
