BiDO: Bilevel Discriminative Learning with Online Synthesis for Object Detection in Foggy Scenes

Object detection in foggy scenes is hampered by severe visibility degradation and the limited availability of trustworthy labels in real fog. Consequently, most methods rely on synthetic-to-real transfer, whose success depends on both the realism of synthesized fog and the detector’s ability to generalize under inevitable distribution mismatch. We present BiDO, a bilevel discriminative learning framework that performs online synthesis while learning a joint dehazing-and-detection model. BiDO couples synthesis and task learning through bilevel optimization, using task-driven signals to adversarially regulate the synthesis process during training. By introducing stronger uncertainty via online synthesis, BiDO favors flatter solutions and yields more robust generalization to real fog. We evaluate BiDO on three representative foggy-scene detection benchmarks under standard synthetic-to-real protocols and observe consistent improvements over prior state-of-the-art approaches. Additional algorithmic studies further highlight the advantages of online synthesis and demonstrate that BiDO can be integrated with different detector architectures. Code will be released if this work can be accepted.