Abstract: Systems, and method and computer readable media that store instructions for calculating signatures, utilizing signatures and the like.

Abstract: A method for responding to a query is implemented on at least one computing device and includes: receiving at least one query from a user device; determining a context for the at least one query, selecting at least one deep learning network (DLN) of a plurality of DLNs to process the at least one query, where the selecting is based at least on matching the context to the at least one DLN, sending at least a representation of the at least one query and the context to the at least one DLN, receiving at least one response to the at least one query from the at least one DLN, and sending the at least one response to the user device.

Abstract: Systems, and method and computer readable media that store instructions for calculating signatures, utilizing signatures and the like.

Abstract: Systems, and method and computer readable media that store instructions for generating a hybrid representation of a media unit.

Abstract: A method for associating a link to web content with a multimedia content element are provided. The method includes identifying at least one multimedia content element in a web-page; generating, by a signature generator system, a signature for the at least one multimedia content element, wherein the signature generator system includes a plurality of computational cores enabled to receive the multimedia content element and to generate a signature for at least a portion of the multimedia content element, each computational core having properties that are at least partly statistically independent of the other computational cores; matching the generated signature to signatures of web content existing on at least one web server to determine matching web content; determining a link to the matching web content; and modifying the at least one multimedia content by adding the link.

Abstract: A method that may include feeding an input image and downscaled versions of the input image to multiple branches of an object detector calculating, by the multiple branches, candidate bounding boxes; and selecting bounding boxes. The multiple branches comprise multiple shallow neural networks that are followed by multiple region units. Each branch includes a shallow neural network and a region unit. The multiple shallow neural networks are multiple instances of a single trained shallow neural network. The single trained shallow neural network is trained to detect objects having a size that is within a predefined size range and to ignore objects having a size that is outside the predefined size range.

Abstract: A method for detecting road elements that may include (a) detecting predefined identifiers of road elements, in road related information sensed by vehicles; (b) detecting potential identifiers of road elements that differ from the predefined identifiers of road elements, by processing road related information that was acquired by the vehicles during relevant time windows that are related to the detecting of the predefined identifiers; (c) finding actual identifiers of road elements out of the potential identifiers; wherein the findings is based, at least in part, on road related information that was acquired by the vehicles outside the relevant time windows; and (d) updating a database with the actual identifiers.

Abstract: A method for passing a curve, the method may include sensing, by a vehicle sensor, environment information regarding an environment of the vehicle; sensing at least one current propagation parameter of the vehicle; detecting, based on at least the environment information, (a) that the vehicle is about to reach the curve, (b) one or more first road conditions of a first road segment that precedes the curve; determining one or more curve passing propagation parameters to be applied by the vehicle while passing the curve, wherein the determining is based, at least in part, on the one or more first road conditions and on the at least one current propagation parameter; and responding to the determining.

Abstract: There may be provided a method for determining object information related to an object that appears in a media element, the method may include (a) receiving or generating a signature of the media element, wherein the signature may include identifiers, wherein the identifiers may include an object identifier related to the object, wherein the object identifier points to locations of output elements, within an output of a convolutional neural network, of elements of significance in relation to the object; (b) finding a group of contributing portions of the media element that had a significant contribution to the generation, by the convolutional neural network, of the output elements of significance in relation to the object; and (c) determining object information related to the object based on a distribution of the group of the contributing portions of the media element.

Abstract: A method for at least partial autonomous driving based on one or more other vehicles, the method may include (i) detecting a trusted vehicle within an environment of a first vehicle, the first vehicle is destined to follow a first path and reach a first target location, the trusted vehicle is destined to follow a second path; (ii) determining, based on at least a spatial relationship between the first path and the second path, at least one out of: (a) whether to perform an at least partial autonomous driving session that involves at least partially automatically following the trusted vehicle, and (b) whether to suggest to a human driver of the first vehicle to authorize the first vehicle to perform the at least partial autonomous driving session; and responding to the determination.

Abstract: A method that may include training a student ODNN to mimic a teacher ODNN. The training may include calculating a teacher student detection loss that is based on a pre-bounding-box output of the teacher ODNN. The pre-bounding-box output of the teacher ODNN is a function of pre-bounding-box outputs of different ODNNs that belong to the teacher ODNN. The method may also include detecting one or more objects in an image, by feeding the image to the trained student ODNN; outputting by the trained student ODNN a student pre-bounding-box output; and calculating one or more bounding boxes based on the student pre-bounding-box output.

Abstract: A method that may include training a student ODNN to mimic a teacher ODNN. The training may include calculating a teacher student detection loss that is based on a pre-bounding-box output of the teacher ODNN. The pre-bounding-box output of the teacher ODNN is a function of pre-bounding-box outputs of different ODNNs that belong to the teacher ODNN. The method may also include detecting one or more objects in an image, by feeding the image to the trained student ODNN; outputting by the trained student ODNN a student pre-bounding-box output; and calculating one or more bounding boxes based on the student pre-bounding-box output.

Abstract: Systems, and method and computer readable media that store instructions for configuring spanning elements of a signature generator.

Abstract: Systems, and method and computer readable media that store instructions for calculating signatures, utilizing signatures and the like.

Abstract: A system and method for recognizing characters embedded in multimedia content are provided. The method includes extracting at least one image of at least one character from a received multimedia content item; identifying a natural language character corresponding to the at least one image of the at least one character, wherein the identification is performed by a deep content classification (DCC) system; and storing the identified natural language character in a data warehouse.

Abstract: A method for determining driving policies for a vehicle includes: receiving, in real-time during a trip of a vehicle, at least a set of input multimedia content elements captured by at least one sensor deployed in proximity to the vehicle, determining a plurality of possible future scenarios based at least on the set of input multimedia content elements, determining a probability score for each of the plurality of possible future scenarios, determining at least one driving policy according to at least the probability score for at least one of the plurality of possible future scenarios, and controlling the vehicle according to the at least one driving policy.

Abstract: A method that includes (a) monitoring a physiological state of a driver to provide physiological state information; (b) receiving or generating an indication about a dangerous event; (c) searching for a predicting physiological parameter that is associated with a predicting pattern that was indicative of an occurrence of the dangerous event; and (d) responding to the when finding of the predicting physiological parameter.

Abstract: A method for operating adjustable headlights of a vehicle, the method may include sensing, by a vehicle night-vision sensor, an environment of the vehicle to provide sensed information; searching, in the sensed information, for a headlight adjustment event identifier, the headlight adjustment event identifier identifies a future occurrence of a headlight adjustment event, the headlight adjustment event requires an adjustment of a lighting pattern formed by at least one of the adjustable highlights of the vehicle; and when finding the headlight adjustment event identifier then adjusting the lighting pattern according to the headlight adjustment event, wherein the adjusting begins before the future occurrence of the headlight adjustment event or immediately at a beginning of the headlight adjustment event.

Abstract: Systems, and method and computer readable media that store instructions for calculating signatures, utilizing signatures and the like.

Abstract: A method for crowd separation, the method may include receiving or generating a signature of an image of a first plurality of pedestrians, wherein the image comprises regions, wherein the signature of the image comprises descriptors of the regions, wherein each descriptor of a region is associated with a region and comprises a set of identifiers that identify content included in the region and in a vicinity of the region; detecting, within the descriptors of the regions, unique combinations of identifiers that are indicative of spatial relationships between the regions and bounding boxes that surround pedestrians of the first plurality of pedestrians; wherein the unique combinations are learnt during a supervised machine learning process that is fed with test images of densely positioned pedestrians; and locating the pedestrians of the first plurality of pedestrians based, at least in part, on the spatial relationships related to detected unique combinations and to locations of the regions.