Abstract: An air conditioner indoor unit includes an electric control box, a cleaner device, and an electrical wire. The cleaner device includes a filter screen rail assembly and a cleaning assembly configured to drive a filter screen to move on the filter screen rail assembly to clean the filter screen. The cleaning assembly includes an electrical wire connection end. The electrical wire is arranged at the cleaning assembly and the filter screen rail assembly. One end of the electrical wire is connected to the electrical wire connection end, and another end of the electrical wire is connected to the electric control box.

Abstract: The present invention discloses a flexible dot matrix bonding apparatus and an adaptive clamping method for a disk-type planar component. The apparatus mainly comprises three components: a substrate, connecting rods and an auxiliary support. The threaded holes in a circumferential array are uniformly distributed in the substrate, and the connecting rods are fixed at different circumferential positions of the substrate in a threaded connection manner, which play a role in bonding and supporting the disk-type planar component. The auxiliary support is matched with the substrate, so that a workpiece after turning can be taken out without damage. According to detection results of machining deformation of the disk-type planar component, the installation positions of the connecting rods on the substrate are adjusted accordingly, thus changing bonding positions of the disk-type planar component.

Abstract: Embodiments of the present disclosure relate to model training based on synthetic data. According to example embodiments of the present disclosure, synthetic images are generated by providing respective text prompts into a text-to-image generation model. Respective training labels associated with the synthetic images are also generated based on the used text prompts. A target model, which is configured to perform an image classification task, is trained based at least in part on the synthetic images and the associated training labels. Through this solution, a large scale of synthetic images can be automatically obtained and applicable for training a model for image classification, to improve the model performance with data-scare setting or in the case of model pre-training where the training data amount matters.

Abstract: Implementations of the present disclosure relate to methods, devices, and computer program products for data augmentation. In the method, mixed data is generated from first data and second data, and the mixed data comprises a first portion from the first data and a second portion from the second data. An attention map is obtained for the mixed data based on distributions of the first and second portions in the mixed data, here the attention map describes contributions of the first and second data to the mixed data. A label is determined for the mixed data based on the attention map and a first label for the first data and a second label for the second data. With these implementations, the label is determined based on the contributions of the first and second images in an accurate and effective way, and thus has a value that is much closer to the ground true.

Abstract: An image reconstruction method includes: an image feature corresponding to a first image in video data and an image feature corresponding to each second image adjacent to the first image are acquired; feature optimization processing is performed on the image feature of the first image and the image feature of the second image to obtain a first optimized feature corresponding to the first image and a second optimized feature corresponding to the second image; feature fusion processing is performed on the first optimized feature and the second optimized feature according to an adjacency matrix between the first optimized feature and the second optimized feature to obtain a fused feature; and image reconstruction processing is performed on the first image using the fused feature to obtain a reconstructed image corresponding to the first image.