no code implementations • 24 May 2024 • Xuanjia Zhao, Jian Guan, Congyi Fan, Dongli Xu, Youtian Lin, Haiwei Pan, Pengming Feng
Drag-based image editing using generative models provides precise control over image contents, enabling users to manipulate anything in an image with a few clicks.
1 code implementation • CVPR 2023 • Jinhong Deng, Dongli Xu, Wen Li, Lixin Duan
Self-training approaches recently achieved promising results in cross-domain object detection, where people iteratively generate pseudo labels for unlabeled target domain samples with a model, and select high-confidence samples to refine the model.
1 code implementation • CVPR 2022 • Dongli Xu, Jinhong Deng, Wen Li
However, a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet been developed. In this work, we revisit the average precision (AP)loss and reveal that the crucial element is that of selecting the ranking pairs between positive and negative samples. Based on this observation, we propose two strategies to improve the AP loss.
no code implementations • 14 Oct 2019 • Donglin Zhan, Shiyu Yi, Dongli Xu, Xiao Yu, Denglin Jiang, Siqi Yu, Haoting Zhang, Wenfang Shangguan, Weihua Zhang
In this paper, we first proposed a general adaptive transfer learning framework for multi-view time series data, which shows strong ability in storing inter-view importance value in the process of knowledge transfer.