Xerox Corporation

Amazon Technologies, Inc.

Conduent Business Services, LLC

Eleonora Vig

Adrien Gaidon

Michael Donoser

Loris Bazzani

Naila Murray

Angel Noe Martinez Gonzalez

Qiao Wang

Filip Saina

César Roberto De Souza

Erhan Gundogdu

Antonio M Lopez

Amaia Salvador Aguilera

Saumya Jetley

Yuxin Hou

Systems and techniques are generally described for attribute-based content selection and search. In some examples, a graphical user interface (GUI) may display an image of a first product comprising a plurality of visual attributes. In some further examples, the GUI may display at least a first control button with data identifying a first visual attribute of the plurality of visual attributes. In some cases, a first selection of the first control button may be received. In some examples, a first plurality of products may be determined based at least in part on the first selection of the first control button. The first plurality of products may be determined based on a visual similarity to the first product, and a visual dissimilarity to the first product with respect to the first visual attribute. In some examples, the first plurality of products may be displayed on the GUI.

Attribute-based content selection and search

Systems and techniques are generally described for generating visually blended recommendation grids. In some examples, a selection of a first item and a second item displayed on a display may be received. In various examples, the first item may be displayed in a first element of a grid and the second item may be displayed in a second element of the grid. In some examples, a third element of the grid that is disposed between the first element and the second element along an axis of the grid may be determined. In various examples, a third item may be determined for display in the third element of the grid based at least in part on a blended representation of an embedding of the first item and an embedding of the second item. The third item may be displayed in the third element of the grid.

Visual blending of content

A system and method are suited for assessing video performance analysis. A computer graphics engine clones real-world data in a virtual world by decomposing the real-world data into visual components and objects in one or more object categories and populates the virtual world with virtual visual components and virtual objects. A scripting component controls the virtual visual components and the virtual objects in the virtual world based on the set of real-world data. A synthetic clone of the video sequence is generated based on the script controlling the virtual visual components and the virtual objects. The real-world data is compared with the synthetic clone of the video sequence and a transferability of conclusions from the virtual world to the real-world is assessed based on this comparison.

Generating a virtual world to assess real-world video analysis performance

Methods and systems for online domain adaptation for multi-object tracking. Video of an area of interest can be captured with an image-capturing unit. The video (e.g., video images) can be analyzed with a pre-trained object detector utilizing online domain adaptation including convex multi-task learning and an associated self-tuning stochastic optimization procedure to jointly adapt online all trackers associated with the pre-trained object detector and a pre-trained category-level model from the trackers in order to efficiently track a plurality of objects in the video captured by the image-capturing unit.

Online domain adaptation for multi-object tracking

A computer-implemented video classification method and system are disclosed. The method includes receiving an input video including a sequence of frames. At least one transformation of the input video is generated, each transformation including a sequence of frames. For the input video and each transformation, local descriptors are extracted from the respective sequence of frames. The local descriptors of the input video and each transformation are aggregated to form an aggregated feature vector with a first set of processing layers learned using unsupervised learning. An output classification value is generated for the input video, based on the aggregated feature vector with a second set of processing layers learned using supervised learning.

System and method for video classification using a hybrid unsupervised and supervised multi-layer architecture

A method for generating a system for predicting saliency in an image and method of use of the prediction system are described. Attention maps for each of a set of training images are used to train the system. The training includes passing the training images though a neural network and optimizing an objective function over the training set which is based on a distance measure computed between a first probability distribution computed for a saliency map output by the neural network and a second probability distribution computed for the attention map for the respective training image. The trained neural network is suited to generation of saliency maps for new images.

End-to-end saliency mapping via probability distribution prediction

A tracking system and method are suited to tracking multiple of objects of different categories in a video sequence. A sequence of video frames is received and a set of windows is extracted from each frame in turn, based on a computed probability that the respective window contains an object, without reference to any specific category of object. For each of these windows, a feature representation is extracted. A trained detector for a selected category detects windows that constitute targets in that category, based on the respective feature representations. More than one detector can be used when there is more than one category of objects to be tracked. A target-specific appearance model is generated for each of the targets (e.g., learned or updated, if the target is present in a prior frame). The detected targets are tracked over one or more subsequent frames based on the target-specific appearance models of the targets.

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Munich, Germany

Eleonora Vig