Search Results for author: Yi Shan

Found 7 papers, 0 papers with code

ProgressFace: Scale-Aware Progressive Learning for Face Detection

no code implementations ECCV 2020 Jiashu Zhu, Dong Li, Tiantian Han, Lu Tian, Yi Shan

In this work, we propose a novel scale-aware progressive training mechanism to address large scale variations across faces.

Curriculum Learning Face Detection

RankDetNet: Delving Into Ranking Constraints for Object Detection

no code implementations CVPR 2021 Ji Liu, Dong Li, Rongzhang Zheng, Lu Tian, Yi Shan

To this end, we comprehensively investigate three types of ranking constraints, i. e., global ranking, class-specific ranking and IoU-guided ranking losses.

3D Object Detection Classification +1

Cross-Dataset Collaborative Learning for Semantic Segmentation

no code implementations21 Mar 2021 Li Wang, Dong Li, Yousong Zhu, Lu Tian, Yi Shan

Particularly, with the same architecture of PSPNet (ResNet-18), our method outperforms the single-dataset baseline by 5. 65\%, 6. 57\%, and 5. 79\% of mIoU on the validation sets of Cityscapes, BDD100K, CamVid, respectively.

Semantic Segmentation

Improving Low-Precision Network Quantization via Bin Regularization

no code implementations ICCV 2021 Tiantian Han, Dong Li, Ji Liu, Lu Tian, Yi Shan

Such bin regularization (BR) mechanism encourages the weight distribution of each quantization bin to be sharp and approximate to a Dirac delta distribution ideally.

Quantization

DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators

no code implementations20 Feb 2019 Yu Xing, Shuang Liang, Lingzhi Sui, Xijie Jia, Jiantao Qiu, Xin Liu, Yushun Wang, Yu Wang, Yi Shan

On the Xilinx ZU2 @330 MHz and ZU9 @330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by na\"ive implementations without optimizations, and the throughput is further improved up to 1. 26x by leveraging heterogeneous optimizations in DNNVM.

Deep Image: Scaling up Image Recognition

no code implementations13 Jan 2015 Ren Wu, Shengen Yan, Yi Shan, Qingqing Dang, Gang Sun

We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning.

Data Augmentation

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