The present disclosure generally pertains to an object detection circuitry configured to: obtain first feature data which are based on first sensing data of a first sensor; compare the first feature data to a first predetermined feature model being representative of a predefined object, wherein the first predetermined feature model is specific for the first sensor, thereby generating first object probability data; obtain second feature data which are based on second sensing data of a second sensor; compare the second feature data to a second predetermined feature model being representative of the predefined object, wherein the second predetermined feature model is specific for the second sensor, thereby generating second object probability data; and combine the first and the second object probability data, thereby generating combined probability data for detecting the predefined object.

A monitoring system for a vehicle cabin, a vehicle including such a monitoring system and a method for monitoring a vehicle cabin. The monitoring system includes a first sensor unit, a second sensor unit and a control unit. The first sensor unit is configured to generate image data of the vehicle cabin. The second sensor unit is configured to generate non-image data of the vehicle cabin. The control unit is configured to collect the image data and the non-image data and determine based thereon whether an obstacle is in the vehicle cabin. The control unit is further configured to limit an actuation of a subsystem if the obstacle is disruptive for the actuation of the subsystem.

An indoor navigation method is provided, including: receiving an instruction for navigation, and collecting an environment image; extracting an instruction room feature and an instruction object feature carried in the instruction, and determining a visual room feature, a visual object feature, and a view angle feature based on the environment image; fusing the instruction object feature and the visual object feature with a first knowledge graph representing an indoor object association relationship to obtain an object feature, and determining a room feature based on the visual room feature and the instruction room feature; and determining a navigation decision based on the view angle feature, the room feature, and the object feature.

Methods and systems for determining human-object interactions (HOIs) in images are provided. The method includes receiving an image. The method further includes detecting at least one human in the image, and at least one object in the image. The method further includes creating one or more proposals, wherein each proposal includes a human of the at least one human and an object of the at least one object. The method further includes determining whether an HOI exist in each of the one or more proposals. In some embodiments, the method further includes generating a mask for each proposal of the one or more proposals in which an HOI is determined to exist, the mask generated based on the determined HOI. In some embodiment, the HOI determination is based on one or more of extracted human feature information, extracted object appearance feature information, and extracted spatial feature information.

A system and a method for super-resolution image processing in remote sensing are disclosed. One or more sets of multi-temporal images with an input resolution and one or more first target images with a first output resolution are generated from one or more data sources. The first output resolution is higher than the input resolution. Each set of multi-temporal images is processed to improve an image match in the corresponding set of multi-temporal images. The one or more sets of multi-temporal images are associated with the one or more first target images to generate a training dataset. A deep learning model is trained using the training dataset. The deep learning model is provided for subsequent super-resolution image processing.

Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to adapt a computing device to classify physical features in a deployment environment. In a particular implementation, computing resources may be selectively de-allocated from at least one of one or more elements of a computing architecture based, at least in part, on assessed impacts to the one or more elements of the computing architecture.

Various embodiments of the disclosure relate to an electronic device for controlling a predefined function based on a response time of an external electronic device on a user input, and a method thereof. The electronic device includes: a memory configured to store one or more applications; a communication module comprising communication circuitry configured to communicate with an external electronic device; and a processor, wherein the processor is configured to control the electronic device to: receive an input; generate first control data for controlling at least one application among the one or more applications using a first recognition method based at least on the input; transmit at least part of the input to the external electronic device through the communication module, wherein the external electronic device is configured to generate second control data for controlling the at least one application using a second recognition method based at least on the input; identify a time that passes until the second control data is received after the at least part of the input is transmitted to the external electronic device; control the at least one application using the first control data based on the passing time satisfying a first predefined condition; and control the at least one application using the second control data based on the passing time satisfying a second predefined condition.

Implementations generally recognize clinical activity using multiple cameras. In some implementations, a method includes obtaining a plurality of videos of a plurality of objects in an environment. The method further includes determining one or more key points for each object of the plurality of objects. The method further includes recognizing activity information based on the one or more key points. The method further includes computing workflow information based on the activity information.

The present disclosure describes techniques of detecting objects in a video. The techniques comprises extracting features from each frame of the video; generating a first attentive feature by applying a first attention model on at least some of features extracted from any particular frame among the plurality of frames, wherein the first attention model identifies correlations between a plurality of locations in the particular frame by computing relationships between any two locations among the plurality of locations; generating a second attentive feature by applying a second attention model on at least one pair of features at different levels selected from the features extracted from the particular frame, wherein the second attention model identifies a correlation between at least one pair of locations corresponding to the at least one pair of features; and generating a representation of an object included in the particular frame.

A mechanism is described for facilitating embedding of human labeler influences in machine learning interfaces in computing environments, according to one embodiment. A method of embodiments, as described herein, includes detecting sensor data via one or more sensors of a computing device, and accessing human labeler data at one or more databases coupled to the computing device. The method may further include evaluating relevance between the sensor data and the human labeler data, where the relevance identifies meaning of the sensor data based on human behavior corresponding to the human labeler data, and associating, based on the relevance, human labeler data with the sensor data to classify the sensor data as labeled data. The method may further include training, based on the labeled data, a machine learning model to extract human influences from the labeled data, and embed one or more of the human influences in one or more environments representing one or more physical scenarios involving one or more humans.