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公开(公告)号：US20230066021A1
公开(公告)日：2023-03-02
申请号：US18049742
申请日：2022-10-26
Inventor： Yunhao WANG , Bin ZHANG , Chao LI , Yan PENG , Song CHEN , Song XUE , Yuan FENG , Shumin HAN
Abstract: A method is provided that includes: segmenting an image to be detected into a plurality of image blocks; generating a feature representation of the image to be detected based on the plurality of image blocks; mapping the feature representation by using a preset parameter set to obtain a plurality of feature maps of the image to be detected; and determining a position and a class of a target object in the image to be detected based on the plurality of feature maps.
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公开(公告)号：US20220301131A1
公开(公告)日：2022-09-22
申请号：US17743057
申请日：2022-05-12
Inventor： Jingwei LIU , Yi GU , Xuhui LIU , Xiaodi WANG , Shumin HAN , Yuan FENG , Ying XIN , Chao LI , Bin ZHANG , Honghui ZHENG , Xiang LONG , Yan PENG , Errui DING , Yunhao WANG
Abstract: A method for generating a sample image includes: obtaining an initial image size of an initial image; obtaining a plurality of reference images by processing the initial image based on different reference processing modes; obtaining an image to be processed by fusing the plurality of reference images; and determining a target sample image from images to be processed based on the initial image size.
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Patent Agency Ranking
公开(公告)号：US20230008696A1
公开(公告)日：2023-01-12
申请号：US17939364
申请日：2022-09-07
Inventor： Yunhao WANG , Bin ZHANG , Ying XIN , Yuan FENG , Shumin HAN
IPC: G06T5/00 , G06V10/22 , G06V10/774 , G06T5/50
Abstract: The present disclosure provides a method for incrementing a sample image, an electronic device, and a computer readable storage medium. A specific implementation comprises: acquiring a first convolutional feature of an original sample image; determining, according to a region generation network and the first convolutional feature, a candidate region and a first probability that the candidate region contains a target object; determining a target candidate region from the candidate region based on the first probability, and mapping the target candidate region back to the original sample image to obtain an intermediate image; and performing image enhancement processing on a portion of the intermediate image corresponding to the target candidate region and/or performing image blur processing on a portion of the intermediate image corresponding to a non-target candidate region to obtain an incremental sample image.
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4.发明公开公开(公告)号：US20240193923A1
公开(公告)日：2024-06-13
申请号：US17908070
申请日：2022-01-29
Inventor： Xiaodi WANG , Shumin HAN , Yuan FENG , Ying XIN , Yi GU , Bin ZHANG , Chao LI , Xiang LONG , Honghui ZHENG , Yan PENG , Zhuang JIA , Yunhao WANG
IPC: G06V10/778
CPC classification number: G06V10/778 , G06V2201/07
Abstract: A method of training a target object detection model includes: extracting a plurality of feature maps of a sample image according to a training parameter, fusing the plurality of feature maps to obtain at least one fused feature map, and obtaining an information of a target object based on the at least one fused feature map, by using the target object detection model; determining a loss of the target object detection model based on the information of the target object and a tag information of the sample image, and adjusting the training parameter according to the loss of the target object detection model. A method of detecting a target object and an apparatus are also provided.
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公开(公告)号：US20230196716A1
公开(公告)日：2023-06-22
申请号：US18173689
申请日：2023-02-23
Inventor： Yuan FENG , Zhun SUN , Honghui ZHENG , Ying XIN , Bin ZHANG , Chao LI , Yunhao WANG , Shumin HAN
IPC: G06V10/44 , G06V10/774 , G06V10/764
CPC classification number: G06V10/443 , G06V10/774 , G06V10/764
Abstract: A method for training a multi-target image-text matching model and an image-text retrieval method are provided. The method for training the multi-target image-text matching model includes: obtaining a plurality of training samples that include sample pairs each including a sample image and a sample text, the sample image including a plurality of targets; obtaining, for each of the plurality of training samples, a heat map corresponding to the sample text in the training sample, the heat map representing a region of the target in the sample image that corresponds to the sample text; and training an image-text matching model based on a plurality of the sample texts and corresponding heat maps to obtain the multi-target image-text matching model.
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公开(公告)号：US20230068025A1
公开(公告)日：2023-03-02
申请号：US17981965
申请日：2022-11-07
Inventor： Yan PENG , Xiang LONG , Honghui ZHENG , Zhuang JIA , Bin ZHANG , Xiaodi WANG , Ying XIN , Yi GU , Yunhao WANG , Chao LI , Yuan FENG , Shumin HAN
Abstract: A method for generating a road annotation, a device, and a storage medium are provided. The method may include: generating a road quantity and a road width in a tag picture; generating, for each road in the tag picture, a start point and an end point of the road; generating at least one point between the start point and the end point; drawing, for two adjacent points, a line segment from a previous point to a next point, where a width of the line segment is equal to the road width; and generating slanted box annotation information based on a coordinate of the previous point and a coordinate of the next point, where the slanted box annotation information includes an intersection point of diagonal lines, a width, a height and a slant angle of a slanted box.
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公开(公告)号：US20220036068A1
公开(公告)日：2022-02-03
申请号：US17504188
申请日：2021-10-18
Inventor： Yan PENG , Xiang LONG , Honghui ZHENG , Zhuang JIA , Bin ZHANG , Xiaodi WANG , Ying XIN , Yi GU , Yunhao WANG , Chao LI , Yuan FENG , Shumin HAN
Abstract: The disclosure provides a method for recognizing an image, an apparatus for recognizing an image, an electronic device and a storage medium. An image to be processed is obtained. The number of first channels of the image is greater than the number of second channels of a red-green-blue (RGB) image. For each pixel of the image, a semantic type of the pixel is determined based on a value of the pixel on each channel. A recognition result of the image is generated based on the image and the semantic type of each pixel.
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