  In incremental object detection, knowledge distillation has been proven to be
an effective way to alleviate catastrophic forgetting. However, previous works
focused on preserving the knowledge of old models, ignoring that images could
simultaneously contain categories from past, present, and future stages. The
co-occurrence of objects makes the optimization objectives inconsistent across
different stages since the definition for foreground objects differs across
various stages, which limits the model's performance greatly. To overcome this
problem, we propose a method called ``Bridge Past and Future'' (BPF), which
aligns models across stages, ensuring consistent optimization directions. In
addition, we propose a novel Distillation with Future (DwF) loss, fully
leveraging the background probability to mitigate the forgetting of old classes
while ensuring a high level of adaptability in learning new classes. Extensive
experiments are conducted on both Pascal VOC and MS COCO benchmarks. Without
memory, BPF outperforms current state-of-the-art methods under various
settings. The code is available at https://github.com/iSEE-Laboratory/BPF.
