Probabilistic Anchor Assignment

Probabilistic anchor assignment (PAA) adaptively separates a set of anchors into positive and negative samples for a GT box according to the learning status of the model associated with it. To do so we first define a score of a detected bounding box that reflects both the classification and localization qualities. We then identify the connection between this score and the training objectives and represent the score as the combination of two loss objectives. Based on this scoring scheme, we calculate the scores of individual anchors that reflect how the model finds useful cues to detect a target object in each anchor. With these anchor scores, we aim to find a probability distribution of two modalities that best represents the scores as positive or negative samples as in the Figure.

Under the found probability distribution, anchors with probabilities from the positive component are high are selected as positive samples. This transforms the anchor assignment problem to a maximum likelihood estimation for a probability distribution where the parameters of the distribution is determined by anchor scores. Based on the assumption that anchor scores calculated by the model are samples drawn from a probability distribution, it is expected that the model can infer the sample separation in a probabilistic way, leading to easier training of the model compared to other non-probabilistic assignments. Moreover, since positive samples are adaptively selected based on the anchor score distribution, it does not require a pre-defined number of positive samples nor an IoU threshold.