Various embodiments provide an apparatus, a method, and a computer program product. 1. An apparatus incudes at least one processor; and at least one non-transitory memory includes computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: perform an overfitting operation, at an encoder side, to obtain an overfitted probability model, wherein overfitting comprises one or more training operations applied to a probability model, wherein one or more parameters of the probability model are trained; use the overfitted probability model to provide probability estimates to a lossless codec or a substantially lossless codec for encoding data or a portion of the data; and signal information to a decoder on whether to perform the overfitting operation at the decoder side.

Various embodiments provide an apparatus, a method, and a computer program product. 1. An apparatus incudes at least one processor; and at least one non-transitory memory includes computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: perform an overfitting operation, at an encoder side, to obtain an overfitted probability model, wherein overfitting comprises one or more training operations applied to a probability model, wherein one or more parameters of the probability model are trained; use the overfitted probability model to provide probability estimates to a lossless codec or a substantially lossless codec for encoding data or a portion of the data; and signal information to a decoder on whether to perform the overfitting operation at the decoder side.

A sketch-based image retrieval method, device and system, to improve accuracy of image searching from a scene sketch image. For example, the image retrieval method, device and system can be used to retrieve a target scene image from a collection of stored images in a storage (i.e., an image collection). The image retrieval method includes: segmenting the scene sketch image using an image segmentation module into semantic object-level instances, and fine-grained features are obtained for each object instance, generating an attribute graph which integrates the fine-grained features for each semantic object instance detected from the query scene sketch image, generating a feature graph by using a graph encoder module from the attribute graph, and computing a similarity or distance between the feature graphs of the query scene sketch image and the scene images in the image collection by a graph matching module and the most similar scene images are returned.

Systems and methods for tracking interacting objects may acquire, with a sensor, and two or more images associated with two or more time instances. A processor may generate image data from the two or more images. The processor may apply an extended Probability Occupancy Map (POM) algorithm to the image data to obtain probability of occupancy for a container class of potentially interacting objects, probability of occupancy for a containee class of the potentially interacting objects, and a size relationship of the potentially interacting objects, over a set of discrete locations on a ground plane for each time instance. The processor may estimate trajectories of an object belonging to each of the two classes by determining a solution of a tracking model on the basis of the occupancy probabilities and a set of rules describing the interaction between objects of different or the same classes.

A method for partially occluded object detection includes obtaining a response map for a detection window of an input image, the response map based on a trained model and including a root layer and a parts layer. The method includes determining visibility flags for each root cell of the root layer and each part of the parts layer. The visibility flag is one of visible or occluded. The method includes determining an occlusion penalty for each root cell with a visibility flag of occluded and for each part with a visibility flag of occluded. The occlusion penalty is based on a location of the root cell or the part with respect to the detection window. The method determines a detection score for the detection window based on the visibility flags and the occlusion penalties and generates an estimated visibility map for object detection based on the detection score.

A method for constructing a dictionary to represent data from a training data set comprising: modeling the data as a linear combination of columns; modeling outliers in the data set via deterministic outlier vectors; formatting the training data set in matrix form for processing; defining an underlying structure in the data set; quantifying a similarity across the data; building a Laplacian matrix; using group-Lasso regularizers to succinctly represent the data; choosing scalar parameters for controlling the number of dictionary columns used to represent the data and the number of elements of the training data set identified as outliers; using BCD and PG methods on the vector-matrix-formatted data set to estimate a dictionary, corresponding expansion coefficients, and the outlier vectors; and using a length of the outlier vectors to identify outliers in the data.

A method and system for modeling trends in crop yields is provided. In an embodiment, the method comprises receiving, over a computer network, electronic digital data comprising yield data representing crop yields harvested from a plurality of agricultural fields and at a plurality of time points; in response to receiving input specifying a request to generate one or more particular yield data: determining one or more factors that impact yields of crops that were harvested from the plurality of agricultural fields; decomposing the yield data into decomposed yield data that identifies one or more data dependencies according to the one or more factors; generating, based on the decomposed yield data, the one or more particular yield data; generating forecasted yield data or reconstructing the yield data by incorporating the one or more particular yield data into the yield data.

The present disclosure relates to navigational systems for vehicles. In one implementation, such a navigational system may a first output from a first sensor and a second output from a second sensor; identify a target object in the first output; and determine, based on the first output, a detected driving condition associated with the target object and whether the condition triggers a navigational constraint. If the navigational constraint is triggered, the system may cause a first navigational adjustment. If the navigational constraint is not triggered, the system may determine whether a representation of the target object is included in the second output. If the representation of the target object is included in the second output, the system may cause a second navigational adjustment. If the representation of the target object is not included in the second output, the system may forego any navigational adjustments.

A system for characterizing content relating to a desired outcome is disclosed. The disclosed system can be configured to identify context included in content collected from various content sources, map the identified context into graph nodes and graph edges connecting the graph nodes, identify one or more features of the identified context and adjust at least one of: a graph node and a graph edge based on the identified one or more features, identify a graph incorporating the graph nodes, the graph edges, and at least one of an adjusted graph node and an adjusted graph edge, and provide a recommendation for at least one action for achieving the desired outcome based on the identified graph.

A multimodal sensing system includes various devices that work together to automatically classify an action. A video camera captures a sequence of digital images. At least one other sensor device captures other sensed data (e.g., motion data). The system will extract video features from the digital images so that each extracted image feature is associated with a time period. It will extract other features from the other sensed data so that each extracted other feature is associated with a time period. The system will fuse a group of the extracted video features and a group of the extracted other features to create a fused feature representation for a time period. It will then analyze the fused feature representation to identify a class, access a data store of classes and actions to identify an action that is associated with the class, and save the identified action to a memory device.