1 code implementation • 31 Jan 2023 • Jiaming Han, Yuqiang Ren, Jian Ding, Ke Yan, Gui-Song Xia
As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples, the learned models are usually biased to base classes and sensitive to the variance of novel examples.
1 code implementation • CVPR 2022 • Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, Gui-Song Xia
Thus, unknown objects in low-density regions can be easily identified with the learned unknown probability.
no code implementations • 2 Aug 2021 • Nenglun Chen, Xingjia Pan, Runnan Chen, Lei Yang, Zhiwen Lin, Yuqiang Ren, Haolei Yuan, Xiaowei Guo, Feiyue Huang, Wenping Wang
We study the problem of weakly supervised grounded image captioning.
1 code implementation • CVPR 2020 • Xingjia Pan, Yuqiang Ren, Kekai Sheng, Wei-Ming Dong, Haolei Yuan, Xiaowei Guo, Chongyang Ma, Changsheng Xu
However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task.