Noise-resistant object detection with noisy annotations
Systems and methods are provided for training object detectors of a neural network model with a mixture of label noise and bounding box noise. According to some embodiments, a learning framework is provided which jointly optimizes object labels, bounding box coordinates, and model parameters by performing alternating noise correction and model training. In some embodiments, to disentangle label noise and bounding box noise, a two-step noise correction method is employed. In some examples, the first step performs class-agnostic bounding box correction by minimizing classifier discrepancy and maximizing region objectness. In some examples, the second step uses dual detection heads for label correction and class-specific bounding box refinement.
1. A computer-implemented method for training a neural network model for object detection, the method comprising:
receiving data for one or more images and corresponding annotations for bounding boxes and labels associated with the images, wherein the annotations include label noise and bounding box noise;
performing, using diverged classification heads, bounding box correction to correct the bounding box noise regardless of the label noise;
performing, using the diverged classification heads, label correction to correct the label noise;
performing, using bounding box regression heads, class-specific bounding box refinement on the corrected bounding boxes; and
training the neural network model for object detection using the corrected labels and refined bounding boxes.
2. The method of claim 1 , wherein performing bounding box correction comprises producing respective sets of class predictions by each classification head.
3. The method of claim 1 , wherein performing bounding box correction comprises maximizing objectness of a region covered by the bounding box.
4. The method of claim 1 , wherein the neural network model comprises a Region-based Convolutional Neural Network (R-CNN).
5. The method of claim 1 , wherein the data for one or more images and corresponding annotations are received in one or more mini-batches.
6. The method of claim 1 , wherein performing label correction comprises producing respective sets of softmax predictions over object classes.
7. The method of claim 1 , wherein training the neural network model comprises optimizing a classification loss for the diverged classification heads.
8. The method of claim 2 , wherein performing bounding box correction comprises minimizing the discrepancy between the respective sets of class predictions produced by the classification heads.
9. The method of claim 5 , wherein training the neural network model for object detection is performed for each mini-batch of the one or more mini-batches.
10. The method of claim 6 , wherein performing label correction comprises averaging the softmax predictions to create a soft label.
11. The method of claim 10 , wherein performing label correction comprises applying a sharpening function on the soft label.
12. A non-transitory machine-readable medium comprising executable code which when executed by one or more processors associated with a computer are adapted to cause the one or more processors to perform a method for training a neural network model for object detection comprising:
receiving data for one or more images and corresponding annotations for bounding boxes and labels associated with the images, wherein the annotations include label noise and bounding box noise;
performing, using diverged classification heads, bounding box correction to correct the bounding box noise regardless of the label noise;
performing, using the diverged classification heads, label correction to correct the label noise;
performing, using bounding box regression heads, class-specific bounding box refinement on the corrected bounding boxes; and
training the neural network model for object detection using the corrected labels and refined bounding boxes.
13. The non-transitory machine-readable medium of claim 12 , wherein performing bounding box correction comprises producing respective sets of class predictions by each classification head.
14. The non-transitory machine-readable medium of claim 13 , wherein performing bounding box correction comprises minimizing the discrepancy between the respective sets of class predictions produced by the classification heads.
15. The non-transitory machine-readable medium of claim 13 , wherein performing bounding box correction comprises maximizing objectness of a region covered by the bounding box.
16. The non-transitory machine-readable medium of claim 13 , wherein the neural network model comprises a Region-based Convolutional Neural Network (R-CNN).
17. The non-transitory machine-readable medium of claim 13 , wherein the data for one or more images and corresponding annotations are received in one or more mini-batches.
18. The non-transitory machine-readable medium of claim 13 , wherein performing label correction comprises producing respective sets of softmax predictions over object classes.
19. The non-transitory machine-readable medium of claim 13 , wherein training the neural network model comprises optimizing a classification loss for the diverged classification heads.
20. The non-transitory machine-readable medium of claim 17 , wherein training the neural network model for object detection is performed for each mini-batch.
21. The non-transitory machine-readable medium of claim 18 , wherein performing label correction comprises averaging the softmax predictions to create a soft label.
22. The non-transitory machine-readable medium of claim 21 , wherein performing label correction comprises applying a sharpening function on the soft label.