Abstract: A model constructing method for a neural network model applicable for image recognition processing is disclosed. The model constructing method includes the following operation: updating, by a processor, a plurality of connection variables between a plurality of layers of the neural network model, according to a plurality of inputs and a plurality of outputs of the neural network model. The plurality of outputs represent a plurality of image recognition results. The plurality of connection variables represent a plurality of connection intensities between each two of the plurality of layers.

Abstract: An image processing method includes the following steps: dividing an object block into a two-dimensional image; identifying at least one view hotspot in a viewing field corresponding to pupil gaze direction; receiving the view hotspot and an indicator signal; wherein the indicator signal is used to remark the object block; and generating a mask block that corresponds to the object block according to the view hotspot; wherein the indicator signal determines the label of the mask block.

Abstract: The disclosure provides a method for generating action according to an audio signal and an electronic device. The method includes: receiving an audio signal and extracting a high-level audio feature therefrom; extracting a latent audio feature from the high-level audio feature; in response to determining that the audio signal corresponds to a beat, obtaining a joint angle distribution matrix based on the latent audio feature; in response to determining that the audio signal corresponds to a music, obtaining a plurality of designated joint angles corresponding to a plurality of joint points based on the joint angle distribution matrix; and adjusting a joint angle of each of the joint points on the avatar according to the designated joint angles.

Abstract: The disclosure provides a method for generating action according to an audio signal and an electronic device. The method includes: receiving an audio signal and extracting a high-level audio feature therefrom; extracting a latent audio feature from the high-level audio feature; in response to determining that the audio signal corresponds to a beat, obtaining a joint angle distribution matrix based on the latent audio feature; in response to determining that the audio signal corresponds to a music, obtaining a plurality of designated joint angles corresponding to a plurality of joint points based on the joint angle distribution matrix; and adjusting a joint angle of each of the joint points on the avatar according to the designated joint angles.

Abstract: An image processing method includes the following steps: receiving a two-dimensional image and segmenting an object block in the two-dimensional image, masking the object block with a color block to generate a mask image; inputting the two-dimensional image and the mask image into a first image processing model, outputting a feature vector from the first image processing model; and inputting the two-dimensional image into a second image processing model to obtain a feature map. The feature map comprises a plurality of feature channel maps, and the feature vector contains a plurality of feature values. Each of the feature channel maps corresponds to one of the feature values in sequence. A weighted feature map is generated according to the feature channel maps and the feature values.

Abstract: An image processing method includes the following steps: receiving a two-dimensional image and segmenting an object block in the two-dimensional image, masking the object block with a color block to generate a mask image; inputting the two-dimensional image and the mask image into a first image processing model, outputting a feature vector from the first image processing model; and inputting the two-dimensional image into a second image processing model to obtain a feature map. The feature map comprises a plurality of feature channel maps, and the feature vector contains a plurality of feature values. Each of the feature channel maps corresponds to one of the feature values in sequence. A weighted feature map is generated according to the feature channel maps and the feature values.

Abstract: An image segmentation method is providing that includes the steps outlined below. A first image corresponding to a first time spot and a second image corresponding to a second time spot are received from a video stream, wherein the second time spot is behind the first time spot. Segmentation is performed on the second image by a segmentation neural network to generate a label probability set. Similarity determination is performed on the first image and the second image by a similarity calculation neural network to generate a similarity probability set. The label probability set and the similarity probability set are concatenated by a concatenating unit to generate a concatenated result. Further inference is performed on the concatenated result by a strategic neural network to generate a label mask.

Abstract: An image processing method includes the following steps: dividing an object block into a two-dimensional image; identifying at least one view hotspot in a viewing field corresponding to pupil gaze direction; receiving the view hotspot and an indicator signal; wherein the indicator signal is used to remark the object block; and generating a mask block that corresponds to the object block according to the view hotspot; wherein the indicator signal determines the label of the mask block.

Abstract: An image segmentation method is providing that includes the steps outlined below. A first image corresponding to a first time spot and a second image corresponding to a second time spot are received from a video stream, wherein the second time spot is behind the first time spot. Segmentation is performed on the second image by a segmentation neural network to generate a label probability set. Similarity determination is performed on the first image and the second image by a similarity calculation neural network to generate a similarity probability set. The label probability set and the similarity probability set are concatenated by a concatenating unit to generate a concatenated result. Further inference is performed on the concatenated result by a strategic neural network to generate a label mask.

Abstract: A model constructing method for a neural network model applicable for image recognition processing is disclosed. The model constructing method includes the following operation: updating, by a processor, a plurality of connection variables between a plurality of layers of the neural network model, according to a plurality of inputs and a plurality of outputs of the neural network model. The plurality of outputs represent a plurality of image recognition results. The plurality of connection variables represent a plurality of connection intensities between each two of the plurality of layers.