Abstract: The image matching apparatus (2000) acquires a ground-view image (20) and an aerial-view image (30). The image matching apparatus (2000) extracts features from the ground-view image (20). The image matching apparatus (2000) extracts features from the aerial-view image (30). The image matching apparatus (2000) extracts a plurality of partial aerial regions (32) from the aerial-view image (30), and extracts features from each partial aerial region (32). The image matching apparatus (2000) computes, for each partial aerial region (32), a combined aerial feature by combining the features of the partial aerial region (32) and the features of the aerial-view image (30). The image matching apparatus (2000) determines, for each partial aerial region (32), whether the partial aerial region (32) matches the ground-view image (20) by comparing the combined aerial feature of the partial aerial region (32) and the features of the ground-view image (20).

Abstract: A training apparatus (2000) acquires a ground-view image (20), an aerial-view image (30), and alignment information (50), and trains a discriminator (10) that extracts a plurality of partial aerial regions (40) from the aerial-view image (30) and determines that the ground-view image (20) matches the aerial-view image (30) when there is the partial aerial region (40) that matches the ground-view image (20). The training apparatus (2000) extracts a correctly-aligned partial aerial region (42) and a mis-aligned partial aerial region (44) from the aerial-view image (30). The correctly-aligned partial aerial region (42) is indicated by the alignment information (50) and corresponds to the ground-view image (20). The mis-aligned partial aerial region (44) partially overlaps the correctly-aligned partial aerial region (42). The training apparatus (2000) trains the discriminator (10) using both the correctly-aligned partial aerial region (42) and the mis-aligned partial aerial region (44) as positive examples.

Abstract: An image augmentation apparatus (2000) performs an image augmentation process on an original training dataset (40) to generate an augmented training dataset (50). The original training dataset (40) includes an original ground-view image (42) and an original aerial-view image (44). The image augmentation process includes a removal process in which partial regions including objects of a specific type are removed. The image augmentation apparatus (2000) performs the image augmentation process on the original ground-view image (42) to generate an augmented ground-view image (52), on the original aerial-view image (44) to generate an augmented aerial-view image (54), or both.

Abstract: The image matching apparatus (2000) comprises a ground-view feature extractor (2020), an aerial-view feature extractor (2040), and a determination unit (2060). The ground-view feature extractor (2020) extracts features from the ground-view image (20) to generate feature maps without attention mechanism and feature maps with attention mechanism. The aerial-view feature extractor (2040) extracts features from the aerial-view image (30) to generate feature maps without attention mechanism and feature maps with attention mechanism. The determination unit (2060) computes a similarity score, which indicates a degree of similarity between the ground-view image (20) and the aerial-view image (30), based on the generated feature maps. Then, the determination unit (2060) determines whether the ground-view image (20) matches the aerial-view image (30) based on the similarity score.

Abstract: An image augmentation apparatus (2000) acquires an original training dataset (40). The original training dataset (40) includes a ground-view image (42) and an original aerial-view image (44). The image augmentation apparatus (2000) performs an image augmentation including a cropping process and a rotation process on the original aerial-view image (44) to generate an augmented aerial-view image (54). In the cropping process, a target region with a circle shape or a regular polygon shape is cropped from the original aerial-view image (44). In the rotation process, the original aerial-view image (44) is rotated. An angle of the rotation is a multiple of a center angle of the target region when the target region has a regular polygon shape. The image augmentation apparatus (2000) outputs an augmented training dataset (50) including the ground-view image (52) and the augmented aerial-view image (54).

Abstract: The training apparatus (2000) performs a first phase training and a second phase training of a discriminator (10). The discriminator (10) acquires a ground-view image and an aerial-view image, and determines whether the acquired ground-view image matches the acquired aerial-view image. The first phase training is performed using a ground-view image and a first level negative example of aerial-view image. The first level negative example of aerial-view image includes scenery of a different type from scenery in the ground-view image. The second phase training is performed using the ground-view image and a second level negative example of aerial-view image. The second level negative example of aerial-view image includes scenery of a same type as scenery in the ground-view image.

Abstract: A method, an apparatus, and a computer program product for wireless communication are provided in connection with improving interactions between various components within a wireless device to enable improved communications between wireless devices. In an example, a wireless device may include an application processor equipped to send first and second amounts of data to be buffered in a buffer associated with a controller, and cease communications with the controller after the second amount of data is sent. In an aspect, the ceasing of communications may include switching to a sleep mode. In an aspect, the amounts of data are larger than a threshold data size, and the buffer includes logically combined dedicated memories from SOCs for a plurality of access technologies controlled by the controller.

Abstract: A method, an apparatus, and a computer program product for wireless communication are provided in connection with improving interactions between various components within a wireless device to enable improved communications between wireless devices. In an example, a wireless device may include an application processor equipped to send first and second amounts of data to be buffered in a buffer associated with a controller, and cease communications with the controller after the second amount of data is sent. In an aspect, the ceasing of communications may include switching to a sleep mode. In an aspect, the amounts of data are larger than a threshold data size, and the buffer includes logically combined dedicated memories from SOCs for a plurality of access technologies controlled by the controller.