Abstract: Inputting an image to a neural network, performing convolution on a current frame included in the image to calculate a current feature map, which is a feature map at a present time, combining a past feature map, which is obtained by performing convolution on a past frame included in the image, and the current feature map, estimating an object candidate area using the combined past feature map and current feature map, estimating positional information and identification information regarding the one or more objects included in the current frame using the combined past feature map and current feature map and the estimated object candidate area, and outputting the positional information and the identification information regarding the one or more objects included in the current frame of the image estimated in the estimating as object detection results are included.

Abstract: Inputting an image to a neural network, performing convolution on a current frame included in the image to calculate a current feature map, which is a feature map at a present time, combining a past feature map, which is obtained by performing convolution on a past frame included in the image, and the current feature map, estimating an object candidate area using the combined past feature map and current feature map, estimating positional information and identification information regarding the one or more objects included in the current frame using the combined past feature map and current feature map and the estimated object candidate area, and outputting the positional information and the identification information regarding the one or more objects included in the current frame of the image estimated in the estimating as object detection results are included.

Abstract: A system and method to receive user input from a human user in a communication session between the human user and a first machine; autonomously determine a sentiment metric of the user input from the human user based on one or more sentiment criteria, wherein the sentiment metric represents an attitude of the human user; autonomously determine a quality metric associated with the communication session; autonomously rank the communication session between human user and first machine based on one or more of the sentiment metric and the quality metric; and determine based on the ranking, to recommend human agent intervention in the communication session between the human user and the first machine.

Abstract: Exemplary embodiments of the present invention relate to an interaction system for a vehicle that can be configured by a user. For example, an interaction system for a vehicle can include an image capture resource to receive eye image data of a user. The interaction system for a vehicle can also include a processor to identify a direction of an eye gaze of the user based on the eye image data. The processor can correlate the eye gaze to a driving experience function (DEF), and the processor can transmit a DEF communication.

Abstract: Exemplary embodiments of the present invention relate to an interaction system for a vehicle. The interaction system includes one or more input devices configured to receive data comprising a characteristic of a driver of the vehicle. The interaction system also includes one or more output devices configured to deliver an output the driver. The interaction system also includes a human machine interface controller. The human machine interface controller receives the data and analyzes the data to identify an emotional state of the driver. The human machine interface controller generates the output based, at least in part, on the emotional state of the driver and sends the output to the one or more output devices. The output includes a simulated emotional state of the human machine interface.