Abstract: Provided is a method and apparatus for aligning a three-dimensional (3D) model. The 3D model alignment method includes acquiring, by a processor, at least one two-dimensional (2D) image including an object, detecting, by the processor, a feature point of the object in the at least one 2D input image using a neural network, estimating, by the processor, a 3D pose of the object in the at least one 2D input image using the neural network, retrieving, by the processor, a target 3D model based on the estimated 3D pose, and aligning, by the processor, the target 3D model and the object based on the feature point.

Abstract: The present disclosure may be embodied as systems and methods for action recognition developed using a multimodal dataset that incorporates both visual data, which facilitates the accurate tracking of movement, and active acoustic data, which captures the micro-Doppler modulations induced by the motion. The dataset includes twenty-one actions and focuses on examples of orientational symmetry that a single active ultrasound sensor should have the most difficulty discriminating. The combined results from three independent ultrasound sensors are encouraging, and provide a foundation to explore the use of data from multiple viewpoints to resolve the orientational ambiguity in action recognition. In various embodiments, recurrent neural networks using long short-term memory (LSTM) or hidden Markov models (HMMs) are disclosed for use in action recognition, for example, human action recognition, from micro-Doppler signatures.

Abstract: An image processing apparatus includes a superpixel extractor configured to extract a plurality of superpixels from an input original image, a backbone network including N feature extracting layers (here, N is a natural number of two or more) which divide the input original image into grids including a plurality of regions and generate an output value including a feature value for each of the divided regions, and a superpixel pooling layer configured to generate a superpixel feature value corresponding to each of the plurality of superpixels using a first output value to an Nth output value output from each of the N feature extracting layers.

Abstract: Methods and devices for offloading and/or aggregation of resources to coordinate uplink transmissions when interacting with different schedulers are disclosed herein. A method in a WTRU includes functionality for coordinating with a different scheduler for each eNB associated with the WTRU's configuration. Disclosed methods include autonomous WTRU grand selection and power scaling, and dynamic prioritization of transmission and power scaling priority.

Abstract: An image processing method includes: obtaining an input image; and performing image conversion processing on the input image by using a generative neural network, to output a converted output image, wherein the generative neural network includes a plurality of processing levels, wherein an output result of an i-th processing level is inputted to an (i+1)-th processing level and a j-th processing level, the j-th processing level further receives an output result of a (j?1)-th processing level, the output result of the (j?1)-th processing level and the output result of the i-th processing level have the same size, wherein i is less than j?1, i and j are positive integers.

Abstract: A method, system, and computer program product provide for video action classification by selecting a first video frame and a first plurality of video frames from a received video to process the first video frame with a 2D convolutional neural network processing pathway to extract spatial features classifying the first video frame, and to process the first plurality of video frames with a 3D convolutional neural network processing pathway to extract spatiotemporal features classifying the first plurality of video frames so that the spatial features are combined with the spatiotemporal features to generate a classification label for the video action.

Abstract: In an aspect, a decision platform that optimizes honey value chain can be provided. The decision platform may receive images of a geographic region including catchment areas, run a first machine learning model with the images as input to identify resources in the catchment areas, run a second machine learning model with the identified resources to predict pollen and nectar concentration in the catchment areas, run a third machine learning model with at least the predicted pollen and nectar concentration to predict honey yield in each of the catchment areas, and determine placement of a swarm to at least one of the catchment areas. The decision platform may also control an unmanned aerial vehicle to guide the swarm to at least one of the catchment areas.

Abstract: An image processing system has a memory storing a video depicting a multi-entity event, a trained reinforcement learning policy and a plurality of domain specific language functions. A graph formation module computes a representation of the video as a graph of nodes connected by edges. A trained machine learning system recognizes entities depicted in the video and recognizes attributes of the entities. Labels are added to the nodes of the graph according to the recognized entities and attributes. The trained machine learning system computes a predicted multi-entity event depicted in the video. For individual ones of the edges of the graph, select a domain specific language function from the plurality of domain specific language functions and assign it to the edge, the selection being made at least according to the reinforcement learning policy. An explanation is formed from the domain specific language functions.

Abstract: Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.

Abstract: A method for predicting a future action of agents in a scene includes assigning a fidelity level to agents observed in the scene. The method also includes recursively predicting future actions of the agents by traversing the scene. A different forward prediction model is used at each recursion level. The method further includes controlling an action of an ego agent based on the predicted future actions of the agents.

Abstract: The disclosed computer-implemented method may include determining a set of filter vectors. Each filter vector in the set of filter vectors may include a set of filter weights associated with at least one portion of an output volume of a resampling operation. The method may also include generating, via a clustering algorithm and based on the set of filter vectors, a filter bank for the resampling operation. The filter bank may include an additional set of filter vectors. The method may further include (1) transmitting the filter bank to a memory module included in a hardware accelerator, and (2) directing the hardware accelerator to execute the resampling operation using an input volume and the filter bank. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.

Abstract: The system and method for hyperspectral target identification provides for sue of a sensor array such that for a 2 by 2 pixel, each pixel is set to a different wavelength by employing a band pass filter, so that one can determine whether a cluster set is of a natural object or a man-made object. Using ratios of the collected energy within the partitioned sub-bands one can make near real-time declarations about targets and in some cases, friend or foe determinations.

Abstract: Embodiments of this disclosure provide a deep learning model used for driving behavior recognition and training apparatus and method thereof. In recognition, the model performs feature extraction by using a plurality of consecutive input images captured from a driving angle of view of a vehicle, and performs temporal and spatial fusion on extracted features by using a recursive neural network. In training, as images captured from a driving angle of view of a vehicle are acquired, the model may be trained. Hence, the model may accurately recognize various classes of driving behaviors.

Abstract: A computer implemented system for generating one or more data structures is described, the one or more data structures representing an unseen composition based on a first category and a second category observed individually in a training data set. During training of a generator, a proposed framework utilizes at least one of the following discriminators—three pixel-centric discriminators, namely, frame discriminator, gradient discriminator, video discriminator; and one object-centric relational discriminator. The three pixel-centric discriminators ensure spatial and temporal consistency across the frames, and the relational discriminator leverages spatio-temporal scene graphs to reason over the object layouts in videos ensuring the right interactions among objects.

Abstract: A technique making use of a few-shot model to determine graphical features present in an image based on a small set of examples with known graphical features. Where a support set including a number of images that each have a known combination of graphical features, the image recognition can identify unknown combinations of those graphical features in any number of query images. In an embodiment of the present disclosure examples of a filled-out form are used to interpret any number of additional filled out versions of the form.

Abstract: A system comprises a combination of digital fingerprint authentication techniques, processes, programs, and hardware to facilitate highly reliable authentication of a wide variety of composite physical objects. “Composite” in this case means that there are distinct regions of the object that must be authenticating individually and in tandem to authenticate the entire object. Preferably, a template is stored that defines for a class of objects what regions must be found, their locations, optionally semantic content of the regions, and other criteria. digital fingerprinting is utilized to locate and attempt to match candidate regions by querying a database of reference object records.

Abstract: We disclose multiband receivers for millimeter-wave devices, which may have reduced size and/or reduced power consumption. One multiband receiver comprises a first band path comprising a first passive mixer configured to receive a first input RF signal having a first frequency and to be driven by a first local oscillator signal having a frequency about ? the first frequency; a second band path comprising a second passive mixer configured to receive a second input RF signal having a second frequency and to be driven by a second local oscillator signal having a frequency about ? the second frequency; and a base band path comprising a third passive mixer configured to receive intermediate RF signals during a duty cycle and to be driven by a third local oscillator signal having a frequency about ? the first frequency or about ? the second frequency during the duty cycle.

Abstract: An image processing system receives an image depicting a bundle of boards. The bundle of boards has a front face that is perpendicular to a long axis of boards and the image is captured at an angle relative to the long axis. The image processing system applies a homographic transformation to estimate a frontal view of the front face and identifies a plurality of divisions between rows in the estimate. For each adjacent pair of the plurality of divisions between rows, a plurality of vertical divisions is identified. The image processing system identifies a set of bounding boxes defined by pairs of adjacent divisions between rows and pairs of adjacent vertical divisions. The image processing system may filter and/or merge some bounding boxes to better match the bounding boxes to individual boards. Based on the bounding boxes, the image processing system determines the number of boards in the bundle.

Abstract: A device comprising a processing unit (3) configured to project at least one camera image (Img1-Img8) from at least one camera (Cam1-Cam8) onto a virtual projection surface (Proj) in order to create a virtual image (ImgV) of the vehicle interior (2).