3 papers
cs.CV2025
Decoupled Distillation to Erase: A General Unlearning Method for Any Class-centric Tasks
Yu Zhou, Dian Zheng, Qijie Mo +3
In this work, we present DEcoupLEd Distillation To Erase (DELETE), a general and strong unlearning method for any class-centric tasks. To derive this, we first propose a theoretica…
cs.CV2025
LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models
Shenghao Fu, Qize Yang, Qijie Mo +5
Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. In this work, we show that an open-vocabulary detector co-training with a…
cs.CV2024
Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object Detection
Qijie Mo, Yipeng Gao, Shenghao Fu +3
In incremental object detection, knowledge distillation has been proven to be an effective way to alleviate catastrophic forgetting. However, previous works focused on preserving t…