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We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold...

Materias / palabras clave: Non-negative matrix factorization; Matrix decomposition; Computer science; Sign (mathematics); Artificial intelligence; Perception; Factorization; Matrix (chemical analysis); Representation (politics); Pattern recognitio...

We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged enco...

We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression o...

We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object det...

The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framew...

Materias / palabras clave: Computer science; Artificial intelligence; Convolutional neural network; Image (mathematics); Pixel; Perception; Transformation (genetics); Ground truth; Transfer (computing); Computer vision; Pattern recognition (psycho...

Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture de...

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessaril...

Introduction * Properties of Population Principal Components * Properties of Sample Principal Components * Interpreting Principal Components: Examples * Graphical Representation of Data Using Principal Components * Choos...

High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuni...

Materias / palabras clave: Artificial intelligence; Computer science; Face detection; AdaBoost; Pattern recognition (psychology); Object-class detection; Classifier (UML); Computer vision; Detector; Face (sociological concept); Facial recognition ...

We present the automated techniques we have developed for new software that optimally detects, deblends, measures and classifies sources from astronomical images: SExtractor (Source Extractor ). We show that a very relia...

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks o...

Materias / palabras clave: Computer science; Robustness (evolution); Artificial intelligence; Hessian matrix; Detector; Computer vision; Invariant (physics); Pattern recognition (psychology); Mathematics; Gene; Telecommunications; Biochemistry

Materias / palabras clave: Artificial intelligence; Robustness (evolution); Computer science; Detector; Hessian matrix; Computer vision; Pattern recognition (psychology); Feature matching; Mathematics; Image (mathematics); Applied mathematics; Gen...

Materias / palabras clave: Rectangle; Artificial intelligence; Segmentation; Aerial image; Computer science; Pattern recognition (psychology); Image (mathematics); Correlation coefficient; Image segmentation; Insulator (electricity); Computer visi...

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics tod...

Materias / palabras clave: Computer science; Artificial intelligence; Segmentation; Margin (machine learning); Sliding window protocol; Context (archaeology); Path (computing); Image segmentation; Pattern recognition (psychology); Network architec...

Materias / palabras clave: Computer science; Segmentation; Encoder; Artificial intelligence; Image segmentation; Feature (linguistics); Deep learning; Computer vision; Pattern recognition (psychology); Image (mathematics); Operating system; Lingui...

Materias / palabras clave: Softmax function; Computer science; Benchmark (surveying); Classifier (UML); Artificial intelligence; Convolutional neural network; Machine learning; Visualization; Pattern recognition (psychology); Geodesy; Geography

Materias / palabras clave: Voxel; Artificial intelligence; Voxel-based morphometry; Segmentation; Pattern recognition (psychology); Smoothing; Computer science; Gray (unit); Artifact (error); Gaussian; Parametric statistics; Market segmentation

Materias / palabras clave: Neuroscience; Segmentation; Artificial intelligence; Psychology; Pattern recognition (psychology); Computer science; Communication

Materias / palabras clave: Feature selection; Artificial intelligence; Selection (genetic algorithm); Computer science; Feature (linguistics); Pattern recognition (psychology); Machine learning; Linguistics; Philosophy