Patents by Inventor Balamanohar Paluri
Balamanohar Paluri has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Publication number: 20210004662Abstract: Systems, methods, and non-transitory computer-readable media can receive a first content item having a set of frames. A binary hash code that represents the first content item is generated using at least an aggregation model and an iterative quantization hash model, the binary hash code being determined based at least in part on the set of frames of the first content item. The binary hash code is stored, wherein a similarity between the first content item and a second content item is capable of being measured based at least in part on a comparison of the binary hash code of the first content item and a binary hash code of the second content item.Type: ApplicationFiled: September 23, 2020Publication date: January 7, 2021Inventors: Kai Li, Fei Yang, Balamanohar Paluri
-
Patent number: 10878579Abstract: Systems, methods, and non-transitory computer-readable media can obtain a first video frame and a second video frame. The first video frame can be processed using a convolutional neural network to output a first set of feature maps. The second video frame can be processed using the convolutional neural network to output a second set of feature maps. The first set of feature maps and the second set of feature maps can be processed using a spatial matching layer of the convolutional neural network to determine an optical flow for at least one pixel.Type: GrantFiled: December 5, 2018Date of Patent: December 29, 2020Assignee: Facebook, Inc.Inventor: Balamanohar Paluri
-
Patent number: 10817774Abstract: Systems, methods, and non-transitory computer-readable media can receive a first content item having a set of frames. A binary hash code that represents the first content item is generated using at least an aggregation model and an iterative quantization hash model, the binary hash code being determined based at least in part on the set of frames of the first content item. The binary hash code is stored, wherein a similarity between the first content item and a second content item is capable of being measured based at least in part on a comparison of the binary hash code of the first content item and a binary hash code of the second content item.Type: GrantFiled: December 30, 2016Date of Patent: October 27, 2020Assignee: Facebook, Inc.Inventors: Kai Li, Fei Yang, Balamanohar Paluri
-
Patent number: 10650133Abstract: Systems, methods, and non-transitory computer-readable media can detect an operation that causes a challenge response process to be initiated. An image category associated with a recognized category label can be identified. At least one image associated with the image category can be displayed during the challenge response process. The operation can be executed when the challenge response process, based on the at least one image, is successfully completed.Type: GrantFiled: January 23, 2019Date of Patent: May 12, 2020Assignee: Facebook, Inc.Inventors: Nikhil Johri, Trevor M. Pottinger, Balamanohar Paluri
-
Patent number: 10614316Abstract: Aspects determining anomalous events, wherein processors determine a trajectory of tracked movement of an object through an image field of a camera partitioned into a matrix grid of different local units. The aspects generate anomaly confidence decision values for image features extracted from video data of the tracked movement of the object as a function of fitting extracted image features to normal patterns of local motion pattern models defined by dominant distributions of extracted image features. The aspects further extract trajectory features from the video data relative to the trajectory of the tracked movement of the object, and generate global anomaly confidence decision values for the object trajectory as a function of fitting the extracted trajectory features to a normal learned motion trajectory model. The aspects determine anomalous events as a function of the generated global anomaly confidence decision value and the anomaly confidence decision values.Type: GrantFiled: December 7, 2017Date of Patent: April 7, 2020Assignee: International Business Machines CorporationInventors: Ankur Datta, Balamanohar Paluri, Sharathchandra U. Pankanti, Yun Zhai
-
Systems and methods for image object recognition based on location information and object categories
Patent number: 10572771Abstract: Systems, methods, and non-transitory computer-readable media can identify a set of regions corresponding to a geographical area. A collection of training images can be acquired. Each training image in the collection can be associated with one or more respective recognized objects and with a respective region in the set of regions. Histogram metrics for a plurality of object categories within each region in the set of regions can be determined based at least in part on the collection of training images. A neural network can be developed based at least in part on the histogram metrics for the plurality of object categories within each region in the set of regions and on the collection of training images.Type: GrantFiled: June 30, 2017Date of Patent: February 25, 2020Assignee: Facebook, Inc.Inventors: Kevin Dechau Tang, Lubomir Bourdev, Balamanohar Paluri, Robert D. Fergus -
Publication number: 20190279053Abstract: A sample set of images is received. Each image in the sample set may be associated with one or more social cues. Correlation of each image in the sample set with an image class is scored based on the one or more social cues associated with the image. Based on the scoring, a training set of images to train a classifier is determined from the sample set. In an embodiment, an extent to which an evaluation set of images correlates with the image class is determined. The determination may comprise ranking a top scoring subset of the evaluation set of images.Type: ApplicationFiled: November 15, 2018Publication date: September 12, 2019Inventors: Lubomir Bourdev, Balamanohar Paluri
-
Publication number: 20190244367Abstract: Systems, methods, and non-transitory computer-readable media can obtain a first video frame and a second video frame. The first video frame can be processed using a convolutional neural network to output a first set of feature maps. The second video frame can be processed using the convolutional neural network to output a second set of feature maps. The first set of feature maps and the second set of feature maps can be processed using a spatial matching layer of the convolutional neural network to determine an optical flow for at least one pixel.Type: ApplicationFiled: December 5, 2018Publication date: August 8, 2019Inventor: Balamanohar Paluri
-
Patent number: 10360498Abstract: Various embodiments of the present disclosure include systems, methods, and non-transitory computer storage media configured to identify a set of training content items, each of the set of training content items comprising video content. A category may be assigned to each of the set of training content items. A plurality of variations may be provided to the each of the set of training content items. A first content recognition module may be trained in an unsupervised process to associate the plurality of variations of the each of the set of training content items with the category assigned to the each of the set of training content items. A classification layer may be generated based on the training the first content recognition module in the unsupervised process. A second content recognition module may be trained in a supervised process based on the classification layer.Type: GrantFiled: December 18, 2014Date of Patent: July 23, 2019Assignee: Facebook, Inc.Inventors: Robert D. Fergus, Lubomir Bourdev, Balamanohar Paluri, Sainbayar Sukhbaatar
-
Patent number: 10360466Abstract: Systems, methods, and non-transitory computer-readable media can receive an image. One or more concepts depicted in the image are identified based on machine learning techniques. The one or more concepts are filtered based on filtering criteria to identify one or more selected concepts. An image description is generated comprising the one or more selected concepts.Type: GrantFiled: December 27, 2016Date of Patent: July 23, 2019Assignee: Facebook, Inc.Inventors: Shaomei Wu, Lada Ariana Adamic, Jeffrey C. Wieland, Omid Farivar, Hermes Germi Pique Corchs, Matt King, Brett Alden Lavalla, Balamanohar Paluri
-
Publication number: 20190156011Abstract: Systems, methods, and non-transitory computer-readable media can detect an operation that causes a challenge response process to be initiated. An image category associated with a recognized category label can be identified. At least one image associated with the image category can be displayed during the challenge response process. The operation can be executed when the challenge response process, based on the at least one image, is successfully completed.Type: ApplicationFiled: January 23, 2019Publication date: May 23, 2019Inventors: Nikhil Johri, Trevor M. Pottinger, Balamanohar Paluri
-
Patent number: 10255423Abstract: Systems, methods, and non-transitory computer-readable media can detect an operation that causes a challenge response process to be initiated. An image category associated with a recognized category label can be identified. At least one image associated with the image category can be displayed during the challenge response process. The operation can be executed when the challenge response process, based on the at least one image, is successfully completed.Type: GrantFiled: May 9, 2017Date of Patent: April 9, 2019Assignee: Facebook, Inc.Inventors: Nikhil Johri, Trevor M. Pottinger, Balamanohar Paluri
-
Systems and methods for determining video feature descriptors based on convolutional neural networks
Patent number: 10198637Abstract: Systems, methods, and non-transitory computer-readable media can acquire video content for which video feature descriptors are to be determined. The video content can be processed based at least in part on a convolutional neural network including a set of two-dimensional convolutional layers and a set of three-dimensional convolutional layers. One or more outputs can be generated from the convolutional neural network. A plurality of video feature descriptors for the video content can be determined based at least in part on the one or more outputs from the convolutional neural network.Type: GrantFiled: December 20, 2017Date of Patent: February 5, 2019Assignee: Facebook, Inc.Inventors: Du Le Hong Tran, Balamanohar Paluri, Lubomir Bourdev, Robert D. Fergus, Sumit Chopra -
Patent number: 10181195Abstract: Systems, methods, and non-transitory computer-readable media can obtain a first video frame and a second video frame. The first video frame can be processed using a convolutional neural network to output a first set of feature maps. The second video frame can be processed using the convolutional neural network to output a second set of feature maps. The first set of feature maps and the second set of feature maps can be processed using a spatial matching layer of the convolutional neural network to determine an optical flow for at least one pixel.Type: GrantFiled: December 28, 2015Date of Patent: January 15, 2019Assignee: Facebook, Inc.Inventor: Balamanohar Paluri
-
Patent number: 10169686Abstract: A sample set of images is received. Each image in the sample set may be associated with one or more social cues. Correlation of each image in the sample set with an image class is scored based on the one or more social cues associated with the image. Based on the scoring, a training set of images to train a classifier is determined from the sample set. In an embodiment, an extent to which an evaluation set of images correlates with the image class is determined. The determination may comprise ranking a top scoring subset of the evaluation set of images.Type: GrantFiled: August 5, 2013Date of Patent: January 1, 2019Assignee: Facebook, Inc.Inventors: Lubomir Bourdev, Balamanohar Paluri
-
Publication number: 20180189281Abstract: Systems, methods, and non-transitory computer-readable media can receive a first content item having a set of frames. A binary hash code that represents the first content item is generated using at least an aggregation model and an iterative quantization hash model, the binary hash code being determined based at least in part on the set of frames of the first content item. The binary hash code is stored, wherein a similarity between the first content item and a second content item is capable of being measured based at least in part on a comparison of the binary hash code of the first content item and a binary hash code of the second content item.Type: ApplicationFiled: December 30, 2016Publication date: July 5, 2018Inventors: Kai Li, Fei Yang, Balamanohar Paluri
-
Publication number: 20180181832Abstract: Systems, methods, and non-transitory computer-readable media can receive an image. One or more concepts depicted in the image are identified based on machine learning techniques. The one or more concepts are filtered based on filtering criteria to identify one or more selected concepts. An image description is generated comprising the one or more selected concepts.Type: ApplicationFiled: December 27, 2016Publication date: June 28, 2018Inventors: Shaomei Wu, Lada Ariana Adamic, Jeffrey C. Wieland, Omid Farivar, Hermes Germi Pique Corchs, Matt King, Brett Alden Lavalla, Balamanohar Paluri
-
SYSTEMS AND METHODS FOR DETERMINING VIDEO FEATURE DESCRIPTORS BASED ON CONVOLUTIONAL NEURAL NETWORKS
Publication number: 20180114069Abstract: Systems, methods, and non-transitory computer-readable media can acquire video content for which video feature descriptors are to be determined. The video content can be processed based at least in part on a convolutional neural network including a set of two-dimensional convolutional layers and a set of three-dimensional convolutional layers. One or more outputs can be generated from the convolutional neural network. A plurality of video feature descriptors for the video content can be determined based at least in part on the one or more outputs from the convolutional neural network.Type: ApplicationFiled: December 20, 2017Publication date: April 26, 2018Inventors: Du Le Hong Tran, Balamanohar Paluri, Lubomir Bourdev, Robert D. Fergus, Sumit Chopra -
Publication number: 20180107881Abstract: Aspects determining anomalous events, wherein processors determine a trajectory of tracked movement of an object through an image field of a camera partitioned into a matrix grid of different local units. The aspects generate anomaly confidence decision values for image features extracted from video data of the tracked movement of the object as a function of fitting extracted image features to normal patterns of local motion pattern models defined by dominant distributions of extracted image features. The aspects further extract trajectory features from the video data relative to the trajectory of the tracked movement of the object, and generate global anomaly confidence decision values for the object trajectory as a function of fitting the extracted trajectory features to a normal learned motion trajectory model. The aspects determine anomalous events as a function of the generated global anomaly confidence decision value and the anomaly confidence decision values.Type: ApplicationFiled: December 7, 2017Publication date: April 19, 2018Inventors: ANKUR DATTA, BALAMANOHAR PALURI, SHARATHCHANDRA U. PANKANTI, YUN ZHAI
-
Patent number: 9946926Abstract: Systems, methods, and non-transitory computer-readable media can calculate raw scores for a plurality of media items based on a classifier model and a target concept. The plurality of media items are ranked based on the raw scores. A review set of the plurality of media items is determined, the review set comprising a subset of the plurality of media items. Each of the media items of the review set is associated with a content depiction determination. A normalized score formula is calculated based on the raw scores and the content depiction determinations for the media items of the review set.Type: GrantFiled: July 7, 2017Date of Patent: April 17, 2018Assignee: Facebook, Inc.Inventors: Nikhil Johri, Balamanohar Paluri, Lubomir Bourdev