no code implementations • 29 Nov 2022 • Taihong Xiao, ZiRui Wang, Liangliang Cao, Jiahui Yu, Shengyang Dai, Ming-Hsuan Yang
Vision-language foundation models pretrained on large-scale data provide a powerful tool for many visual understanding tasks.
no code implementations • 30 Dec 2019 • Xiaojie Jin, Jiang Wang, Joshua Slocum, Ming-Hsuan Yang, Shengyang Dai, Shuicheng Yan, Jiashi Feng
In this paper, we propose the resource constrained differentiable architecture search (RC-DARTS) method to learn architectures that are significantly smaller and faster while achieving comparable accuracy.
no code implementations • ICCV 2019 • JIyang Gao, Jiang Wang, Shengyang Dai, Li-Jia Li, Ram Nevatia
Comparing to standard Faster RCNN, it contains three highlights: an ensemble of two classification heads and a distillation head to avoid overfitting on noisy labels and improve the mining precision, masking the negative sample loss in box predictor to avoid the harm of false negative labels, and training box regression head only on seed annotations to eliminate the harm from inaccurate boundaries of mined bounding boxes.