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公开(公告)号：US20160071024A1
公开(公告)日：2016-03-10
申请号：US14631124
申请日：2015-02-25
Applicant: SRI International
Inventor： Mohamed R. Amer , Behjat Siddiquie , Ajay Divakaran , Colleen Richey , Saad Khan , Harpreet S. Sawhney
CPC classification number: G06N99/005 , G06K9/6296 , G06N7/005
Abstract: Technologies for analyzing temporal components of multimodal data to detect short-term multimodal events, determine relationships between short-term multimodal events, and recognize long-term multimodal events, using a deep learning architecture, are disclosed.
Abstract translation: 公开了用于分析多模态数据的时间分量以检测短期多模态事件的技术，确定短期多模态事件之间的关系，并使用深度学习架构来识别长期多模态事件。
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42.发明申请公开(公告)号：US20160063692A1
公开(公告)日：2016-03-03
申请号：US14567101
申请日：2014-12-11
Applicant: SRI International
Inventor： Ajay Divakaran , Weiyu Zhang , Qian Yu , Harpreet S. Sawhney
CPC classification number: G06K9/6202 , G06K9/4642 , G06K9/4671 , G06K9/6223 , G06K9/6269 , G06T2207/10024 , G06T2207/30128
Abstract: A food recognition assistant system includes technologies to recognize foods and combinations of foods depicted in a digital picture of food. Some embodiments include technologies to estimate portion size and calories, and to estimate nutritional value of the foods. In some embodiments, data identifying recognized foods and related information are generated in an automated fashion without relying on human assistance to identify the foods. In some embodiments, the system includes technologies for achieving automatic food detection and recognition in a real-life setting with a cluttered background, without the images being taken in a controlled lab setting, and without requiring additional user input (such as user-defined bounding boxes). Some embodiments of the system include technologies for personalizing the food classification based on user-specific habits, location and/or other criteria.
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公开(公告)号：US20130282747A1
公开(公告)日：2013-10-24
申请号：US13737607
申请日：2013-01-09
Applicant: SRI INTERNATIONAL
Inventor： Hui Cheng , Harpreet Singh Sawhney , Ajay Divakaran , Qian Yu , Jingen Liu , Amir Tamrakar , Saad Ali , Omar Javed
IPC: G06F17/30
CPC classification number: G06F17/30823 , G06F17/30023 , G06F17/30784 , G06F17/30817
Abstract: A complex video event classification, search and retrieval system can generate a semantic representation of a video or of segments within the video, based on one or more complex events that are depicted in the video, without the need for manual tagging. The system can use the semantic representations to, among other things, provide enhanced video search and retrieval capabilities.
Abstract translation: 复杂的视频事件分类，搜索和检索系统可以基于视频中描绘的一个或多个复杂事件，而不需要手动标记来生成视频中的视频或片段的语义表示。 该系统可以使用语义表示来提供增强的视频搜索和检索功能。
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公开(公告)号：US20240403728A1
公开(公告)日：2024-12-05
申请号：US18614388
申请日：2024-03-22
Applicant: SRI International
Inventor： Yunye Gong , Yi Yao , Xiao Lin , Ajay Divakaran
Abstract: In general, techniques are described that address the limitations of existing conformal prediction methods for cascaded models. In an example, a method includes receiving a first validation data set for validating performance of an upstream model of the two or more cascaded models and receiving a second validation data set for validating performance of a downstream model of the two or more cascaded models wherein the second validation data set is different than the first validation set; estimating system-level errors caused by predictions of the upstream model based on the first validation data set; estimating system-level errors caused by predictions of the downstream model based on the second validation data set; and generating a prediction confidence interval that indicates a confidence for the system based on the system-level errors caused by predictions of the upstream model and based on the system-level errors caused by predictions of the downstream model.
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45.发明公开公开(公告)号：US20240062042A1
公开(公告)日：2024-02-22
申请号：US18451692
申请日：2023-08-17
Applicant: SRI International
Inventor： Aswin Nadamuni Raghavan , Saurabh Farkya , Jesse Albert Hostetler , Avraham Joshua Ziskind , Michael Piacentino , Ajay Divakaran , Zhengyu Chen
CPC classification number: G06N3/045 , G06F21/566 , G06N3/098 , G06F2221/033
Abstract: In general, the disclosure describes techniques for implementing an MI-based attack detector. In an example, a method includes training a neural network using training data, applying stochastic quantization to one or more layers of the neural network, generating, using the trained neural network, an ensemble of neural networks having a plurality of quantized members, wherein at least one of weights or activations of each of the plurality of quantized members have different bit precision, and combining predictions of the plurality of quantized members of the ensemble to detect one or more adversarial attacks and/or determine performance of the ensemble of neural networks.
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Patent Agency Ranking
公开(公告)号：US11790213B2
公开(公告)日：2023-10-17
申请号：US16439508
申请日：2019-06-12
Applicant: SRI International
Inventor： Yi Yao , Ajay Divakaran , Pallabi Ghosh
Abstract: Techniques are disclosed for identifying multimodal subevents within an event having spatially-related and temporally-related features. In one example, a system receives a Spatio-Temporal Graph (STG) comprising (1) a plurality of nodes, each node having a feature descriptor that describes a feature present in the event, (2) a plurality of spatial edges, each spatial edge describing a spatial relationship between two of the plurality of nodes, and (3) a plurality of temporal edges, each temporal edge describing a temporal relationship between two of the plurality of nodes. Furthermore, the STG comprises at least one of: (1) variable-length descriptors for the feature descriptors or (2) temporal edges that span multiple time steps for the event. A machine learning system processes the STG to identify the multimodal subevents for the event. In some examples, the machine learning system comprises stacked Spatio-Temporal Graph Convolutional Networks (STGCNs), each comprising a plurality of STGCN layers.
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公开(公告)号：US11238631B2
公开(公告)日：2022-02-01
申请号：US16855362
申请日：2020-04-22
Applicant: SRI International
Inventor： Karan Sikka , Ajay Divakaran , Samyak Datta
Abstract: A method, apparatus and system for visual grounding of a caption in an image include projecting at least two parsed phrases of the caption into a trained semantic embedding space, projecting extracted region proposals of the image into the trained semantic embedding space, aligning the extracted region proposals and the at least two parsed phrases, aggregating the aligned region proposals and the at least two parsed phrases to determine a caption-conditioned image representation and projecting the caption-conditioned image representation and the caption into a semantic embedding space to align the caption-conditioned image representation and the caption. The method, apparatus and system can further include a parser for parsing the caption into the at least two parsed phrases and a region proposal module for extracting the region proposals from the image.
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公开(公告)号：US20210390400A1
公开(公告)日：2021-12-16
申请号：US17304163
申请日：2021-06-15
Applicant: SRI International
Inventor： Yi Yao , Ajay Divakaran , Hammad A. Ayyubi
Abstract: Techniques are described for neural networks based on Progressive Neural ODEs (PODEs). In an example, a method to progressively train a neural ordinary differential equation (NODE) model comprises processing, by a machine learning system executed by a computing system, first training data, the first training data having a first complexity, to perform training of a first layer for the NODE model; and after performing the first training, processing second training data, the second training data having a second complexity that is higher than the first complexity, to perform training of a second layer for the NODE model.
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公开(公告)号：US20210295082A1
公开(公告)日：2021-09-23
申请号：US17337093
申请日：2021-06-02
Applicant: SRI International
Inventor： Karan Sikka , Ajay Divakaran , Ankan Bansal
Abstract: A method, apparatus and system for zero shot object detection includes, in a semantic embedding space having embedded object class labels, training the space by embedding extracted features of bounding boxes and object class labels of labeled bounding boxes of known object classes into the space, determining regions in an image having unknown object classes on which to perform object detection as proposed bounding boxes, extracting features of the proposed bounding boxes, projecting the extracted features of the proposed bounding boxes into the space, computing a similarity measure between the projected features of the proposed bounding boxes and the embedded, extracted features of the bounding boxes of the known object classes in the space, and predicting an object class label for proposed bounding boxes by determining a nearest embedded object class label to the projected features of the proposed bounding boxes in the space based on the similarity measures.
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公开(公告)号：US20210056742A1
公开(公告)日：2021-02-25
申请号：US16855362
申请日：2020-04-22
Applicant: SRI International
Inventor： Karan Sikka , Ajay Divakaran , Samyak Datta
Abstract: A method, apparatus and system for visual grounding of a caption in an image include projecting at least two parsed phrases of the caption into a trained semantic embedding space, projecting extracted region proposals of the image into the trained semantic embedding space, aligning the extracted region proposals and the at least two parsed phrases, aggregating the aligned region proposals and the at least two parsed phrases to determine a caption-conditioned image representation and projecting the caption-conditioned image representation and the caption into a semantic embedding space to align the caption-conditioned image representation and the caption. The method, apparatus and system can further include a parser for parsing the caption into the at least two parsed phrases and a region proposal module for extracting the region proposals from the image.
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