Beijing Baidu Netcom Science and Technology Co., Ltd.

Baidu Online Network Technology (beijing) Co., Ltd.

Yuchen Yuan

Jian Wang

Hao Sun

Errui Ding

Feng Zhou

Xiangbo Su

Ming Sun

Zipeng Lu

A human body identification method, an electronic device and a storage medium, related to the technical field of artificial intelligence such as computer vision and deep learning, are provided. The method includes: inputting an image to be identified into a human body detection model, to obtain a plurality of preselected detection boxes; identifying a plurality of key points from each of the preselected detection boxes respectively according to a human body key point detection model, and obtaining a key point score of each of the key points; determining a target detection box from each of the preselected detection boxes, according to a number of the key points whose key point scores meet a key point threshold; and inputting the target detection box into a human body key point classification model, to obtain a human body identification result for the image to be identified.

Human body identification method, electronic device and storage medium

Embodiments of the present disclosure provide a method and apparatus for detecting a target object, an electronic device, and a computer readable storage medium. The method may include: acquiring a to-be-detected image; processing the to-be-detected image using an object detection model including a deformable convolutional layer to obtain an offset prediction result of the deformable convolutional layer; and adjusting a size of an anchor point using the offset prediction result, and determining position information of the target object in the to-be-detected image using the anchor point of the adjusted size.

Method and apparatus for detecting target object, electronic device and storage medium

The present disclosure provides a method and apparatus for training a fine-grained image recognition model, a device and a storage medium. The method comprises: obtaining images as training samples, and respectively obtaining a tag corresponding to each image, the tag including a class to which the image belongs; training according to the training samples and corresponding tags to obtain a fine-grained image recognition model, and performing constraint at a feature level from two dimensions, namely, the class and object parts, during the training, so that the fine-grained image recognition model learns key object parts in the images; upon performing the fine-grained image recognition, inputting a to-be-recognized image to the fine-grained image recognition model, so that the fine-grained image recognition model positions key object parts in the image, and completes fine-grained image classification according to the key object parts, and outputs a classification result. The solution of the present disclosure can be applied to save manpower costs and improve the model training efficiency.

Method and apparatus for training fine-grained image recognition model, fine-grained image recognition method and apparatus, and storage mediums

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Beijing, China

Yuchen Yuan