Abstract: The present disclosure provides a display device and a display method thereof. The display device includes a microlens array including a plurality of microlenses; and a display panel including a plurality of pixel islands, wherein the plurality of pixel islands are arranged in one-to-one correspondence with the plurality of microlenses, each pixel island includes a plurality of sub-pixels, light emitted by the plurality of sub-pixels of each pixel island enters a human eye through a microlens corresponding to the each pixel island and forms an image in the human eye, and regions where images formed by at least two pixel islands in the plurality of pixel islands via microlenses corresponding to the at least two pixel islands are located are connected.

Abstract: A storage rack and a drying device including the storage rack. The storage rack includes a frame and a support beam assembly including a connector seat, a first connector and a support beam. The connector seat is connected to the frame, and the support beam is connected to the connector seat by the first connector. The support beam is connected to the frame by the first connector and the connector seat rather than by direct welding which may cause deformation. It is ensured that the support beam can provide desirable levelness for a shelf panel to be supported thereon. In this way, good levelness of the shelf panel can be achieved at a reduced thickness thereof, resulting in a reduction in economic cost. Moreover, for a detachable shelf panel, its placement and removal can be facilitated.

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: 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: 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: Technologies are provided for generating three-dimensional (3D) skeletons of target objects using images of the target objects captured from different viewpoints. Images of an object (such as a person) can be captured from different camera angles. Feature keypoints of the object can be identified in the captured images. Keypoints that identify a same feature in separate images can be correlated using truncated epipolar lines. For example, depth information for a keypoint can be used to truncate an epipolar line that is created using the keypoint. The correlated feature keypoints can be used to create 3D feature coordinates for the associated features of the object. A 3D skeleton can be generated using the 3D feature coordinates. One or more 3D models can be mapped to the 3D skeleton and rendered. The rendered one or more 3D models can be displayed on one or more display devices.

Abstract: Technologies are provided for generating three-dimensional (3D) skeletons of target objects using images of the target objects captured from different viewpoints. Images of an object (such as a person) can be captured from different camera angles. Feature keypoints of the object can be identified in the captured images. Keypoints that identify a same feature in separate images can be correlated using truncated epipolar lines. For example, depth information for a keypoint can be used to truncate an epipolar line that is created using the keypoint. The correlated feature keypoints can be used to create 3D feature coordinates for the associated features of the object. A 3D skeleton can be generated using the 3D feature coordinates. One or more 3D models can be mapped to the 3D skeleton and rendered. The rendered one or more 3D models can be displayed on one or more display devices.