Abstract:
Systems and techniques are described for performing supervised learning (e.g., semi-supervised learning, self-supervised learning, and/or mixed supervision learning) for optical flow estimation. For example, a method can include obtaining an image associated with a sequence of images and generating an occluded image. The occluded image can include at least one of the image with an occlusion applied to the image and a different image of the sequence of images with the occlusion applied. The method can include determining a matching map based at least on matching areas of the image and the occluded image and, based on the matching map, determining a loss term associated with an optical flow loss prediction associated with the image and the occluded image. The loss term may include a matched loss and/or other loss. Based on the loss term, the method can include training a network configured to determine an optical flow between images.
Abstract:
Certain aspects of the present disclosure provide techniques for machine learning using basis decomposition, comprising receiving a first runtime record, where the first runtime record includes RF signal data collected in a physical space; processing the first runtime record using a plurality of basis machine learning (ML) models to generate a plurality of inferences; aggregating the plurality of inferences to generate a prediction comprising a plurality of coordinates; and outputting the prediction, where the plurality of coordinates indicate a location of a physical element in a physical space.
Abstract:
Aspects described herein provide a method of performing phase selective convolution, including: receiving multi-phase pre-activation activation data; partitioning the multi-phase pre-activation data; applying a first activation function to the set of first phase pre-activation data to form a set of first phase activation output; convolving the set of first phase activation output with a first convolution kernel to form a first phase output feature map; negating the set of second phase activation data; applying a second activation function to the negated set of second phase pre-activation data to form a set of second phase activation output; convolving the set of second phase activation output with a second convolution kernel to form a second phase output feature map; negating the second phase output feature map; and training the neural network based on the first phase output feature map and the second phase output feature map.
Abstract:
Methods, systems, and devices for outputting of codeword bits for transmission prior to loading all input bits. An example encoder may have multiple encoding branches. The encoder may divide the encoding branches into first and second encoding branch subsets, outputs of the first encoding branch subset being independent of inputs to the second encoding branch subset. The encoder may generate first and second subsets of output bits of a codeword in first and second encoding operations, the generating comprising inputting information bits of an information bit-vector and at least one frozen bit into respective encoding branches of the plurality of encoding branches and generating the first subset of output bits using the first encoding branch subset prior to generating the second subset of output bits using the second encoding branch subset. The encoder may output the first subset of output bits prior to outputting the second subset of output bits.
Abstract:
Certain aspects of the present disclosure relate to method and apparatus for wireless communication. In certain aspects, the method generally includes transmitting first control information during a first transmission time interval (TTI), wherein the first control information indicates resources within a TTI allocated for a data transmission, and transmitting the data using the indicated resources. The method further includes transmitting second control information, wherein the second control information also indicates the resources for the data transmission.
Abstract:
The disclosure relates in some aspects to techniques for use in systems where a plurality of devices with different priority levels share a common set of resources for communication (e.g., downlink transmissions). Certain aspects provide a new indication channel and a procedure to signal scheduling information (e.g., priority information). Such information may serve as an indicator for possible new grants. Such information may additionally serve as an indicator for higher-priority scheduling conflicts or include explicit commands that result from conflicts (e.g., conflicts relating to puncturing of resources allocated for transmissions to lower priority devices).
Abstract:
Systems and techniques are described herein for performing optical flow estimation for one or more frames. For example, a process can include determining an optical flow prediction associated with a plurality of frames. The process can include determining a position of at least one feature associated with a first frame and determining, based on the position of the at least one feature in the first frame and the optical flow prediction, a position estimate of a search area for searching for the at least one feature in a second frame. The process can include determining, from within the search area, a position of the at least one feature in the second frame.
Abstract:
Certain aspects of the present disclosure provide techniques for kernel expansion. An input data tensor is received at a first layer in a neural network, and a first convolution is performed for a first kernel, where the first kernel has a size greater than a preferred size. Performing the first convolution comprises generating a plurality of intermediate tensors by performing a plurality of intermediate convolutions using a plurality of intermediate kernels with a size of the preferred size, and accumulating the plurality of intermediate tensors to generate an output tensor for the first convolution.
Abstract:
Methods, systems, and devices for wireless communications are described. In accordance with the described techniques, communicating devices (e.g., an encoder and decoder) may apply an orthogonal cover code to a polar codeword to reduce cross-correlation between different codewords. For example, such techniques may reduce power consumption at a decoding device by providing for earlier decoding termination (e.g., as a result of the reduced cross-correlation). Techniques for generating the cover codes (e.g., on a per-aggregation level basis) and applying the cover codes (e.g., within a search space) are described. Additionally or alternatively, the described techniques may relate to seeding of reference signals used to support decoding of the codewords. Improved orthogonality between reference signal seeds may further suppress codeword recipient ambiguity.
Abstract:
Various techniques provide for identifying and transmitting a first transmission to a UE in a first transmission time interval (TTI), and puncturing a portion of the first transmission with a higher priority second transmission that has a shorter TTI than the first TTI. The punctured portion of the first transmission may then be transmitted in a subsequent portion of the first TTI, concurrently with an originally allocated portion of the first transmission for that subsequent portion of the TTI. A UE may identify the punctured portion of the first transmission, and identify that the punctured portion of the first transmission is being transmitted in the subsequent portion of the first TTI. The UE may decode the received transmissions and merge the punctured portion with other, non-punctured, portions of the first transmission.