Few-shot object detection method

A computer device, comprising a memory, a processor and computer programs stored on the memory and executable on the processor, wherein when the computer programs are executed by the processor, a few-shot object detection method is implemented, the method comprising:

• sending a weight of a backbone network and a weight of a feature pyramid to a detection network, wherein the weight of the backbone network and the weight of the feature pyramid are derived from a visual representation backbone network generated by self-supervised training;
• generating candidate regions, wherein the candidate regions are derived from a result of foreground-and-background view classification and regression of output features of the visual representation backbone network by a region proposal network;
• generating candidate region features of a uniform size using a pooling operator based on the candidate regions, and performing location regression, content classification and fine-grained feature mining on the candidate region features of the uniform size;
• establishing fine-grained positive sample pairs and negative sample pairs through the fine-grained feature mining, and performing comparative learning between fine-grained features of the candidate regions, wherein the fine-grained feature mining comprises a strategy of:
• performing region division on the uniform-sized candidate region features using an even division manner, extracting features of different regions after the division, assigning same labels to region division results from a same candidate region and assigning different labels to region division results from different candidate regions;
• and
• generating a loss function according to the strategy in the fine-grained feature mining, and updating detection network parameters by calculating based on the loss function.

A non-transitory computer-readable storage medium having computer programs stored thereon, wherein when the computer programs are executed by a processor, a few-shot object detection method is implemented, the method comprising:

• sending a weight of a backbone network and a weight of a feature pyramid to a detection network, wherein the weight of the backbone network and the weight of the feature pyramid are derived from a visual representation backbone network generated by self-supervised training;
• generating candidate regions, wherein the candidate regions are derived from a result of foreground-and-background view classification and regression of output features of the visual representation backbone network by a region proposal network;
• generating candidate region features of a uniform size using a pooling operator based on the candidate regions, and performing location regression, content classification and fine-grained feature mining on the candidate region features of the uniform size;
• establishing fine-grained positive sample pairs and negative sample pairs through the fine-grained feature mining, and performing comparative learning between fine-grained features of the candidate regions, wherein the fine-grained feature mining comprises a strategy of:
• performing region division on the uniform-sized candidate region features using an even division manner, extracting features of different regions after the division, assigning same labels to region division results from a same candidate region and assigning different labels to region division results from different candidate regions;
• and
• generating a loss function according to the strategy in the fine-grained feature mining, and updating detection network parameters by calculating based on the loss function.