Provided in the present disclosure are a method and system for facial recognition. The method for facial recognition comprises: acquiring an image of an unshielded area of a face; and utilizing the image of the unshielded area of the face for facial recognition.

Provided in the present disclosure are a method and system for facial recognition. The method for facial recognition comprises: acquiring an image of an unshielded area of a face; and utilizing the image of the unshielded area of the face for facial recognition.

Methods and systems for predictive measures of anti-EGFR therapy response in wild type RAS/EGFR+ samples, e.g., histochemical staining methods for staining EGFR, AREG, and EREG, digital analysis of stained slides, and scoring algorithms that allow prediction of a response to anti-EGFR therapies. Analysis of the stained slides and scoring algorithms may include but are not limited to: a percent tumor cell positivity, computerized clustering algorithms, area density (e.g., area of tumor positive for one or more markers over total tumor area), average intensity (e.g., computerized methodology measuring average gray scale pixel intensity), average intensity broken down according to membrane, cytoplasmic, or punctate staining patterns), or any other appropriate parameter or combination of parameters. The methods of the present invention allow for resolving spatial expression patterns of the ligands and the receptor to determine what patterns are predictive for response to anti-EGFR therapies.

Methods and systems for predictive measures of anti-EGFR therapy response in wild type RAS/EGFR+ samples, e.g., histochemical staining methods for staining EGFR, AREG, and EREG, digital analysis of stained slides, and scoring algorithms that allow prediction of a response to anti-EGFR therapies. Analysis of the stained slides and scoring algorithms may include but are not limited to: a percent tumor cell positivity, computerized clustering algorithms, area density (e.g., area of tumor positive for one or more markers over total tumor area), average intensity (e.g., computerized methodology measuring average gray scale pixel intensity), average intensity broken down according to membrane, cytoplasmic, or punctate staining patterns), or any other appropriate parameter or combination of parameters. The methods of the present invention allow for resolving spatial expression patterns of the ligands and the receptor to determine what patterns are predictive for response to anti-EGFR therapies.

Systems and methods for performing temporal progressive learning for video processing are provided herein. Some examples include receiving a video that includes a plurality of frames, extracting a first subset of frames from the plurality of frames, and inputting the first subset of frames into a model that includes an encoder and a decoder. The examples further include comparing a first output of the model to the first subset of frames and updating the encoder, thereby training the encoder, and extracting a second subset of frames from the plurality of frames. The second subset of frames includes a number of frames that is larger than a number of frames in the first subset of frames. The examples further include inputting the second subset of frames into the model, comparing a second output of the model to the second subset of frames and updating the encoder, thereby further training the encoder.

Systems and methods for performing temporal progressive learning for video processing are provided herein. Some examples include receiving a video that includes a plurality of frames, extracting a first subset of frames from the plurality of frames, and inputting the first subset of frames into a model that includes an encoder and a decoder. The examples further include comparing a first output of the model to the first subset of frames and updating the encoder, thereby training the encoder, and extracting a second subset of frames from the plurality of frames. The second subset of frames includes a number of frames that is larger than a number of frames in the first subset of frames. The examples further include inputting the second subset of frames into the model, comparing a second output of the model to the second subset of frames and updating the encoder, thereby further training the encoder.

The present disclosure provides a caricature generation method capable of expressing detailed and realistic facial exaggerations and allowing a reduction of training labor and cost. A caricature generating method includes: providing a generation network comprising a plurality of layers connected in series including coarse layers of lowest resolutions and pre-trained to be suitable for synthesizing a shape of a caricature and fine layers of highest resolutions and pre-trained to be suitable for tuning a texture of the caricature; applying input feature maps representing an input facial photograph to the coarse layers to generate shape feature maps and deforming the shape feature maps by shape exaggeration blocks to generate deformed shape feature maps; applying the deformed shape feature maps to the fine layers to change a texture represented by the deformed shape feature maps and generate output feature maps; and generating a caricature image according to the output feature map.

The present disclosure provides a caricature generation method capable of expressing detailed and realistic facial exaggerations and allowing a reduction of training labor and cost. A caricature generating method includes: providing a generation network comprising a plurality of layers connected in series including coarse layers of lowest resolutions and pre-trained to be suitable for synthesizing a shape of a caricature and fine layers of highest resolutions and pre-trained to be suitable for tuning a texture of the caricature; applying input feature maps representing an input facial photograph to the coarse layers to generate shape feature maps and deforming the shape feature maps by shape exaggeration blocks to generate deformed shape feature maps; applying the deformed shape feature maps to the fine layers to change a texture represented by the deformed shape feature maps and generate output feature maps; and generating a caricature image according to the output feature map.

A target recognition method and device based on a MASK RCNN network model are disclosed. The method comprises: determining a multi-stage network as a basic network; selecting at least one intermediate layer capable of extracting a feature map from the basic network, and inputting respectively a feature map output by the intermediate layer and a feature map output by an end layer of the basic network to corresponding MASK RCNN recognition networks to construct a network model based on the MASK RCNN, wherein the feature map output by the intermediate layer and the feature map output by the end layer have different sizes; training the MASK RCNN recognition networks with a data set and stopping training until a preset training end condition is satisfied; and recognizing the target using the MASK RCNN recognition networks after trained. This solution is very suitable for small target recognition of a flying UAV.

A target recognition method and device based on a MASK RCNN network model are disclosed. The method comprises: determining a multi-stage network as a basic network; selecting at least one intermediate layer capable of extracting a feature map from the basic network, and inputting respectively a feature map output by the intermediate layer and a feature map output by an end layer of the basic network to corresponding MASK RCNN recognition networks to construct a network model based on the MASK RCNN, wherein the feature map output by the intermediate layer and the feature map output by the end layer have different sizes; training the MASK RCNN recognition networks with a data set and stopping training until a preset training end condition is satisfied; and recognizing the target using the MASK RCNN recognition networks after trained. This solution is very suitable for small target recognition of a flying UAV.