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Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

October 2020

tl;dr: Generalized focal loss that can optimize for any continuous number and distribution. Uses a joint cls-IoU representation to predict the localization quality and the distribution of boxes locations.

Overall impression

The paper follows the method of focal loss (modulating Cross Entropy by L2 loss). Actually cross entropy can be easily extended to regressing any number between 0 and 1, but it just have a very flat bottom. Now generalized focal loss modulates this extended cross entropy by L2 loss.

A recent trend in one-stage detector is to introduce an individual prediction branch to estimate the quality of localization. The center-ness (FOCS and ATSS) or IoU score branch (IoUNet) can be trained separately and used in NMS process. But the quality predictor is inconsistent between training and test. Concretely, the negative bbox does not have IoU supervision and can have extremely high IoU predictions and thus degrades the NMS process.

Bbox boundaries are generally formulated as a Dirac delta function (deterministic) or Gaussian (Gaussian yolo and KL Loss). This paper targets to formulate the boundary as an arbitrarily shaped distribution. This formulation itself reaches the same performance as baseline, but with DFL (distributional focal loss), it is better. --> for loss on a distribution, cf Unsuperpoint.

The encoding of a regression target is similar to one-hot encoding in depth regression network, such as single-modal weighted average (SMWA) and Deoth Coefficient.

The dispersion of the distribution can be used as localization confidence as well. This in a way achieves what uncertainty learning (Gaussian yolo and KL Loss) tries to achieve, but without the uncertainty bit which can be hard to train in practice. --> this is actually exactly what the improved version GFocalV2 does.

Key ideas

  • QFL (quality focal loss): Unifies classification score and IoU quality to be one cls-iou score.
    • The target is dynamically updated online and it is in (0, 1]. For negative samples, the target is 0.
    • So during training, not only the quality of the good predictions get trained with label 1, but the quality of all predicted boxes gets supervision.
  • DFL (distributional focal loss): Directly optimizes a distribution of bbox boundaries. the regression target is quantized into n (n=14) bins. The target is expressed as the integral over the distribution.
    • The regression target is actually prepared in the same way as ATSS. In the above review post, and in Fig. 5(c), it is 0 to 16 in ATSS, 0 to 8 in FCOS. The delta is selected to 1 after ablation study. --> In essence, instead of predicting 4 numbers, now it predict 4 x 16 numbers, classification instead of regression.
    • gIoU loss actually should work quite well. In order to further boosts the performance, 2 nearest bins from the target are selected from the n targets after a softmax layer is used to calculate the loss.

Technical details

  • Bbox classification: why multiple sigmoid vs softmax? --> FCOS
  • The paper is able to generate bimodal distribution for the prediction of ambiguous boundaries. --> maybe we should not do simply integration but rather find the local peak? cf single-modal weighted average (SMWA) and Deoth Coefficient. --> GFocalV2 uses topK prediction to guide the prediction of localization quality. Still it used the mean of the entire distribution to predict the localization.

Notes