Abstract: Systems and methods for intent discovery and video summarization are described. Embodiments of the present disclosure receive a video and a transcript of the video, encode the video to obtain a sequence of video encodings, encode the transcript to obtain a sequence of text encodings, apply a visual gate to the sequence of text encodings based on the sequence of video encodings to obtain gated text encodings, and generate an intent label for the transcript based on the gated text encodings.

Abstract: Software instructions are executed on a processor within a computer system to configure a streaming engine with stream parameters to define a multidimensional array. The stream parameters define a size for each dimension of the multidimensional array and a specified width for a selected dimension of the array. Data is fetched from a memory coupled to the streaming engine responsive to the stream parameters. A stream of vectors is formed for the multidimensional array responsive to the stream parameters from the data fetched from memory. When the selected dimension in the stream of vectors exceeds the specified width, the streaming engine inserts null elements into each portion of a respective vector for the selected dimension that exceeds the specified width in the stream of vectors. Stream vectors that are completely null are formed by the streaming engine without accessing the system memory for respective data.

Abstract: A group captioning system includes computing hardware, software, and/or firmware components in support of the enhanced group captioning contemplated herein. In operation, the system generates a target embedding for a group of target images, as well as a reference embedding for a group of reference images. The system identifies information in-common between the group of target images and the group of reference images and removes the joint information from the target embedding and the reference embedding. The result is a contrastive group embedding that includes a contrastive target embedding and a contrastive reference embedding with which to construct a contrastive group embedding, which is then input to a model to obtain a group caption for the target group of images.

Abstract: Provided is a Face-aware Offset Calculation (FOC) module and method for facial frame interpolation and enhancement and a face video deblurring system and method using the same. The system comprises: a facial frame enhancement device, including a FOC module, for enhancing a target frame; a facial frame interpolation device, including the FOC module, for interpolating the target frame; and a combination device for combining the enhanced target frame with the interpolated target frame.

Abstract: An interactive entity relationship diagram discovers explicitly defined relationships, and also dynamically discovers and represents non-explicit relationships. This entails calculating metadata and references by parsing the queries. An entity relationship diagram thereby provides novel visibility on the queries being related as the basis for a user interface which integrates several configuration utilities.

Abstract: A stream of data is accessed from a memory system using a stream of addresses generated in a first mode of operating a streaming engine in response to executing a first stream instruction. A block cache management operation is performed on a cache in the memory using a block of addresses generated in a second mode of operating the streaming engine in response to executing a second stream instruction.

Abstract: In some examples, a circuit includes an interface configured to couple to a memory that includes a set of outputs to provide a set of data from the memory. The circuit further includes a rotator coupled to the interface that includes a first set of multiplexors that each include a set of inputs coupled to the set of outputs of the interface and an output. The circuit further includes a storage circuit coupled to the rotator that includes a register file coupled to the outputs of the first set of multiplexors an alignment network. The alignment network includes a second set of multiplexors that each include a set of inputs coupled to the register file and an output.

Abstract: Software instructions are executed on a processor within a computer system to configure a steaming engine with stream parameters to define a multidimensional array. The stream parameters define a size for each dimension of the multidimensional array and a specified width for two selected dimensions of the array. Data is fetched from a memory coupled to the streaming engine responsive to the stream parameters. A stream of vectors is formed for the multidimensional array responsive to the stream parameters from the data fetched from memory. When either selected dimension in the stream of vectors exceeds a respective specified width, the streaming engine inserts null elements into each portion of a respective vector for the selected dimension that exceeds the specified width in the stream of vectors. Stream vectors that are completely null are formed by the streaming engine without accessing the system memory for respective data.

Abstract: Devices and methods for treatment of a patient's vasculature are described. Embodiments may include a permeable implant connected to an expandable stent through a plurality of wires. The permeable shell is configured for placement in an aneurysm and the expandable stent is configured for placement in a parent vessel proximal, near, or adjacent the aneurysm.

Abstract: Software instructions are executed on a processor within a computer system to configure a streaming engine with stream parameters to define a multidimensional array. The stream parameters define a size for each dimension of the multidimensional array and a pad value indicator. Data is fetched from a memory coupled to the streaming engine responsive to the stream parameters. A stream of vectors is formed for the multidimensional array responsive to the stream parameters from the data fetched from memory. A padded stream vector is formed that includes a specified pad value without accessing the pad value from system memory.

Abstract: A streaming engine in a system receives a first set of stream parameters into a queue to define a first stream along with an indication of either a queue mode of operation or a speculative mode of operation for the first stream. Acquisition of the first stream then begins. At some point, a second set of stream parameters is received into the queue to define a second stream. When the queue mode of operation was specified for the first stream, the second set of parameters is queued and acquisition of the second stream is delayed until completion of acquisition of the first stream. When the speculative mode of operation was specified for the first stream, acquisition of the first stream is canceled upon receipt of the second set of stream parameters and acquisition of the second stream begins immediately.

Abstract: One aspect of systems and methods for data correction includes identifying a false label from among predicted labels corresponding to different parts of an input sample, wherein the predicted labels are generated by a neural network trained based on a training set comprising training samples and training labels corresponding to parts of the training samples; computing an influence of each of the training labels on the false label by approximating a change in a conditional loss for the neural network corresponding to each of the training labels; identifying a part of a training sample of the training samples and a corresponding source label from among the training labels based on the computed influence; and modifying the training set based on the identified part of the training sample and the corresponding source label to obtain a corrected training set.

Abstract: The present disclosure describes a model for large scale color prediction of objects identified in images. Embodiments of the present disclosure include an object detection network, an attention network, and a color classification network. The object detection network generates object features for an object in an image and may include a convolutional neural network (CNN), region proposal network, or a ResNet. The attention network generates an attention vector for the object based on the object features, wherein the attention network takes a query vector based on the object features, and a plurality of key vector and a plurality of value vectors corresponding to a plurality of colors as input. The color classification network generates a color attribute vector based on the attention vector, wherein the color attribute vector indicates a probability of the object including each of the plurality of colors.

Abstract: Software instructions are executed on a processor within a computer system to configure a steaming engine with stream parameters to define a multidimensional array. The stream parameters define a size for each dimension of the multidimensional array and a pad value indicator. Data is fetched from a memory coupled to the streaming engine responsive to the stream parameters. A stream of vectors is formed for the multidimensional array responsive to the stream parameters from the data fetched from memory. A padded stream vector is formed that includes a specified pad value without accessing the pad value from system memory.

Abstract: A streaming engine in a system receives a first set of stream parameters into a queue to define a first stream along with an indication of either a queue mode of operation or a speculative mode of operation for the first stream. Acquisition of the first stream then begins. At some point, a second set of stream parameters is received into the queue to define a second stream. When the queue mode of operation was specified for the first stream, the second set of parameters is queued and acquisition of the second stream is delayed until completion of acquisition of the first stream. When the speculative mode of operation was specified for the first stream, acquisition of the first stream is canceled upon receipt of the second set of stream parameters and acquisition of the second stream begins immediately.

Abstract: A group captioning system includes computing hardware, software, and/or firmware components in support of the enhanced group captioning contemplated herein. In operation, the system generates a target embedding for a group of target images, as well as a reference embedding for a group of reference images. The system identifies information in-common between the group of target images and the group of reference images and removes the joint information from the target embedding and the reference embedding. The result is a contrastive group embedding that includes a contrastive target embedding and a contrastive reference embedding with which to construct a contrastive group embedding, which is then input to a model to obtain a group caption for the target group of images.

Abstract: Techniques related to executing a plurality of instructions by a processor comprising receiving a first instruction configured to cause the processor to output a first data value to a first address in a first data cache, outputting, by the processor, the first data value to a second address in a second data cache, receiving a second instruction configured to cause a streaming engine associated with the processor to prefetch data from the first data cache, determining that the first data value has not been outputted from the second data cache to the first data cache, stalling execution of the second instruction, receiving an indication, from the second data cache, that the first data value has been output from the second data cache to the first data cache, and resuming execution of the second instruction based on the received indication.

Abstract: Systems and methods for color prediction are described. Embodiments of the present disclosure receive an image that includes an object including a color, generate a color vector based on the image using a color classification network, where the color vector includes a color value corresponding to each of a set of colors, generate a bias vector by comparing the color vector to teach of a set of center vectors, where each of the set of center vectors corresponds to a color of the set of colors, and generate an unbiased color vector based on the color vector and the bias vector, where the unbiased color vector indicates the color of the object.

Abstract: Systems and methods for intent discovery and video summarization are described. Embodiments of the present disclosure receive a video and a transcript of the video, encode the video to obtain a sequence of video encodings, encode the transcript to obtain a sequence of text encodings, apply a visual gate to the sequence of text encodings based on the sequence of video encodings to obtain gated text encodings, and generate an intent label for the transcript based on the gated text encodings.

Abstract: A system includes a memory controller, multiple memories coupled to the memory controller, and multiple controlling components coupled to the memory controller. The memory controller calculates an error correction code (ECC) syndrome of a first type for a segment of data; stores the segment of data and the ECC syndrome of the first type in a first memory of the multiple memories; receives a request from a controlling component of the multiple controlling components directed to the segment of data, the controlling component implementing an ECC syndrome of a second type; transforms the ECC syndrome of the first type for the segment of data to the ECC syndrome of the second type; detects a number of errors, if any, present in the segment of data; and takes further action depending on how many errors are detected.