68 skills found · Page 1 of 3
Avinash793 / Panoramic Image StitchingCreate panorama image using invariant features from given set of overlapping images.
microsoft / MS LumosTools to compare metrics between datasets, accounting for population differences and invariant features.
kirill-vish / Beyond INetCode for experiments for "ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy"
nianticlabs / Rectified Features[ECCV 2020] Single image depth prediction allows us to rectify planar surfaces in images and extract view-invariant local features for better feature matching
pvangoor / GIFTGeneral Invariant Feature Tracker (GIFT) detects and tracks image features such as corners and points in common camera set-ups such as monocular, stereo, and multi-view.
TurtleTools / GeometricusA structure-based, alignment-free embedding approach for proteins. Can be used as input to machine learning algorithms.
sophiaas / Spectral UniversalityOfficial PyTorch and JAX Implementation of "Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks"
nayeemrizve / Invariance Equivariance"Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning" by Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah (CVPR 2021)
sheoranhimansh / AutoPanoramaImplementation of IJCV 2007 David Lowe (Automatic Panoramic Image Stitching using Invariant Features)
ayanavasarkar / Multiple Image StitchingA Python and OpenCV implementation of Image Stitching using Brute Force Matcher and ORB feature descriptures.
qq456cvb / SPRINPRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features
bangawayoo / Nlp WatermarkingRobust natural language watermarking using invariant features
liuQuan98 / GCL[ICCV 23] Density-invariant Features for Distant Point Cloud Registration
SonyCSLParis / Cgae InvarConvolutional gated autoencoder for learning transposition-invariant features from audio
danielmachinelearning / SentimentAnalysis CNN RNNAn example of a sentiment analysis program, used on the IMDB movie review dataset. The program uses the Keras deep learning library. First, it imports the IMDB movie set, then uses a Convolutional Neural Network (CNN) to extract out invariant features detailing a good or bad movie review. Finally, it passes it through a recursive neural network (RNN) using LSTMs to learn the state transitions of the invariant features.
zhang201882 / MTF CRNNInspired by the convolutional recurrent neural network(CRNN) and inception, we propose a multiscale time-frequency convolutional recurrent neural network (MTF-CRNN) for audio event detection. Our goal is to improve audio event detection performance and recognize target audio events that have different lengths and accompany the complex audio background. We exploit multi-groups of parallel and serial convolutional kernels to learn high-level shift invariant features from the time and frequency domains of acoustic samples. A two-layer bi-direction gated recurrent unit) based on the recurrent neural network is used to capture the temporal context from the extracted high-level features. The proposed method is evaluated on the DCASE2017 challenge dataset. Compared to other methods, the MTF-CRNN achieves one of the best test performances for a single model without pre-training and without using a multi-model ensemble approach.
guglielmocamporese / Learning Invariances In Speech RecognitionIn this work I investigate the speech command task developing and analyzing deep learning models. The state of the art technology uses convolutional neural networks (CNN) because of their intrinsic nature of learning correlated represen- tations as is the speech. In particular I develop different CNNs trained on the Google Speech Command Dataset and tested on different scenarios. A main problem on speech recognition consists in the differences on pronunciations of words among different people: one way of building an invariant model to variability is to augment the dataset perturbing the input. In this work I study two kind of augmentations: the Vocal Tract Length Perturbation (VTLP) and the Synchronous Overlap and Add (SOLA) that locally perturb the input in frequency and time respectively. The models trained on augmented data outperforms in accuracy, precision and recall all the models trained on the normal dataset. Also the design of CNNs has impact on learning invariances: the inception CNN architecture in fact helps on learning features that are invariant to speech variability using different kind of kernel sizes for convolution. Intuitively this is because of the implicit capability of the model on detecting different speech pattern lengths in the audio feature.
sam0x17 / DSegInvariant Superpixel Features for Object Detection
qq456cvb / PanoromaFully C++ implementation of "Automatic Panoramic Image Stitching using Invariant Features"
turian / DonatefacesExtract faces from video clips; generate training data for pose-invariant face features
