Facebook, Inc.

Meta Platforms, Inc.

Piotr Dollar

Ronan Stéfan Collobert

Pedro Henrique Oliveira Pinheiro

Ross Girshick

Michael F Cohen

Jason George McHugh

Johannes Peter Kopf

Kaiming He

Xinlei Chen

Saining Xie

Georgia Gkioxari

Yanghao Li

In particular embodiments, a computing system may access a plurality of images for pre-training a first machine-learning model that includes an encoder and a decoder. Using each image, the system may pre-train the model by dividing the image into a set a patches, selecting a first subset of the patches to be visible and a second subset of the patches to be masked during the pre-training, processing, using the encoder, the first subset of patches to generate corresponding first latent representations, processing, using the decoder, the first latent representations corresponding to the first subset of patches and mask tokens corresponding to the second subset of patches to generate reconstructed patches corresponding to the second subset of patches, the reconstructed patches and the first subset of patches being used to generate a reconstructed image, and updating the model based on comparisons between the image and the reconstructed image.

Masked autoencoders for computer vision

In one embodiment, a feature map of an image having h&#xd7;w pixels and a patch having one or more pixels of the image are received. The patch has been processed by a first set of layers of a convolutional neural network and contains an object centered within the patch. The patch is then processed using the feature map and one or more pixel classifiers of a classification layer of a deep-learning model, where the classification layer includes h&#xd7;w pixel classifiers, with each pixel classifier corresponding to a respective pixel of the patch. Each of the pixel classifiers used to process the patch outputs a respective value indicating whether the corresponding pixel belongs to the object centered in the patch.

Generating refined object proposals using deep learning models

In one embodiment, a method includes a computing system accessing a training image. The system may generate a feature map for the training image using a first neural network. The system may identify a region of interest in the feature map and generate a regional feature map for the region of interest based on sampling locations defined by a sampling region. The sampling region and the region of interest may correspond to the same region in the feature map. The system may generate an instance segmentation mask associated with the region of interest by processing the regional feature map using a second neural network. The second neural network may be trained using the instance segmentation mask. Once trained, the second neural network is configured to generate instance segmentation masks for object instances depicted in images.

Method and system for using machine-learning for object instance segmentation

Systems, methods, and non-transitory computer-readable media can generate an initial alpha mask for an image based on machine learning techniques. A plurality of uncertain pixels is defined in the initial alpha mask. For each uncertain pixel in the plurality of uncertain pixels, a binary value is assigned based on a nearest certain neighbor determination.

Systems and methods for image matting

In one embodiment, a plurality of patches of an image are processed using a first-pass of a first deep-learning model to generate object-level information for each of the patches. Each patch includes one or more pixels of the image. Using a second-pass of the first deep-learning model, a respective object proposal is generated for each of the plurality of patches of the image. The second-pass takes as input the first-pass output, and the generated respective object proposals comprise pixel-level information for each of the patches. Using a second deep-learning model, a respective score is computed for each object proposal. The second deep-learning model takes as input the first-pass output, and the object score includes a likelihood that the respective patch of the object proposal contains an entire object.

Generating object proposals using deep-learning models

In one embodiment a plurality of patches of an image are processed, using a first set of layers of a convolutional neural network, to output a plurality of object proposals associated with the plurality of patches of the image. Each patch includes one or more pixels of the image. Each object proposal includes a prediction as to a location of an object in the respective patch. Using a second set of layers of the convolutional neural network, the plurality of object proposals outputted by the first set of layers are processed to generate a plurality of refined object proposals. Each refined object proposal includes pixel-level information for the respective patch of the image. The first layer in the second set of layers of the convolutional neural network takes as input the plurality of object proposals outputted by the first set of layers. Each layer after the first layer in the second set of layers takes as input the output of a preceding layer in the second set of layers combined with the output of a respective layer of the first set of layers.

Generating refined object proposals using deep-learning models

In one embodiment, a plurality of patches of an image are processed using a first deep-learning model to detect a plurality of features associated with the first patch of the image. Each patch includes one or more pixels of the image. Using a second deep-learning model, a respective object proposal is generated for each of the plurality of patches of the image. The second deep-learning model takes as input the plurality of detected features associated with the respective patch of the image, and each object proposal includes a prediction as to a location of an object in the patch. Using a third deep-learning model, a respective score is computed for each object proposal generated using the second deep-learning model. The third deep-learning model takes as input the plurality of detected features associated with the respective patch of the image, and the object score may include a likelihood that the patch contains an entire object.

Growing community of inventors

San Mateo, CA, United States of America

Piotr Dollar