classification task. Most notably, the teacher model’s output may yield misleading guidance to the student model [@chen2017learning]. The teacher regression result can be contradictory to the ground truth labels, also the output from the teacher regression module is unbounded. To address these issues, [@chen2017learning] proposed a procedure to only adopt teacher’s output at beneficial times. For a one-stage object detection setting, [@mehta2018object] optimized the student model with a similar loss function to Eq. \[eq:a\].

In this paper, we propose a novel and bio-inspired way of adopting the teacher model’s knowledge. Namely, temporally estimating the expectation of object labels, their sizes, and shapes based on the previously observed frames or $E[y_i| \alpha_1,  \alpha_2, ... , \alpha_{i-1}]$ where $y_i$ is our objects label and $\alpha$ our observations. This expectation changes in time by camera or objects movements, and/or the changing of the field of view. Here, we utilize this extracted knowledge to improve object detection performance. Unlike the previous work such as [@mehta2018object; @chen2017learning], we are not aiming to improve the feature extractor and/or the general knowledge of the student model. We optimize the decoder inside the student model to adapt it to the current environment. It is done by increasing the 