5 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.CV2023
Boosting Long-tailed Object Detection via Step-wise Learning on Smooth-tail Data
Na Dong, Yongqiang Zhang, Mingli Ding +1
Real-world data tends to follow a long-tailed distribution, where the class imbalance results in dominance of the head classes during training. In this paper, we propose a frustrat…
cs.CV2022★ 2 cited
Open World DETR: Transformer based Open World Object Detection
Na Dong, Yongqiang Zhang, Mingli Ding +1
Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision. Furthermore, the e…
cs.CV2022★ 5 cited
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning
Na Dong, Yongqiang Zhang, Mingli Ding +1
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes.…