Efficient pose estimation through iterative refinement

A processing system, comprising:

• one or more processors individually or collectively configured to execute instructions stored on one or more memories and to cause the processing system to:
• process input data with a feature extraction stage of a machine learning model to generate a feature map;
• apply an attention map to the feature map to generate an augmented feature map;
• select a batch normalization layer from a plurality of batch normalization layers based on a loop count;
• process the augmented feature map with a refinement stage of the machine learning model to generate a refined feature map, wherein in order to process the augmented feature map with the refinement stage of the machine learning model, the one or more processors are further configured to cause the processing system to apply the batch normalization layer to the augmented feature map;
• process the refined feature map with a first regression stage of the machine learning model to generate multi-dimensional task output data;
• and
• process the refined feature map with an attention stage of the machine learning model to generate an updated attention map.

A non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform a method, the method comprising:

• processing input data with a feature extraction stage of a machine learning model to generate a feature map;
• applying an attention map to the feature map to generate an augmented feature map;
• selecting a batch normalization layer, from a plurality of batch normalization layers, based on a loop count;
• processing the augmented feature map with a refinement stage of the machine learning model to generate a refined feature map, wherein processing the augmented feature map with the refinement stage of the machine learning model comprises applying the batch normalization layer to the augmented feature map;
• processing the refined feature map with a first regression stage of the machine learning model to generate multi-dimensional task output data;
• and
• processing the refined feature map with an attention stage of the machine learning model to generate an updated attention map.

A processing system, comprising:

• means for processing input data with a feature extraction stage of a machine learning model to generate a feature map;
• means for applying an attention map to the feature map to generate an augmented feature map;
• means for selecting a batch normalization layer, from a plurality of batch normalization layers, based on a loop count;
• means for processing the augmented feature map with a refinement stage of the machine learning model to generate a refined feature map, wherein means for processing the augmented feature map with the refinement stage of the machine learning model comprise means for applying the batch normalization layer to the augmented feature map;
• means for processing the refined feature map with a first regression stage of the machine learning model to generate multi-dimensional task output data;
• and
• means for processing the refined feature map with an attention stage of the machine learning model to generate an updated attention map.