Search Results for author: Zili Liu

Found 10 papers, 6 papers with code

Training-Time-Friendly Network for Real-Time Object Detection

6 code implementations2 Sep 2019 Zili Liu, Tu Zheng, Guodong Xu, Zheng Yang, Haifeng Liu, Deng Cai

Experiments on MS COCO show that our TTFNet has great advantages in balancing training time, inference speed, and accuracy.

Object object-detection +1

SCALoss: Side and Corner Aligned Loss for Bounding Box Regression

1 code implementation1 Apr 2021 Tu Zheng, Shuai Zhao, Yang Liu, Zili Liu, Deng Cai

In this paper, we propose Side Overlap~(SO) loss by maximizing the side overlap of two bounding boxes, which puts more penalty for low overlapping bounding box cases.

object-detection Object Detection +1

Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast

no code implementations27 Feb 2022 Zili Liu, Kun Hao, Xiaoyi Geng, Zhenwei Shi

Tropical cyclone (TC) is an extreme tropical weather system and its trajectory can be described by a variety of spatio-temporal data.

Tropical Cyclone Track Forecasting

DeepPhysiNet: Bridging Deep Learning and Atmospheric Physics for Accurate and Continuous Weather Modeling

1 code implementation4 Jan 2024 Wenyuan Li, Zili Liu, Keyan Chen, Hao Chen, Shunlin Liang, Zhengxia Zou, Zhenwei Shi

Next, we construct hyper-networks based on deep learning methods to directly learn weather patterns from a large amount of meteorological data.

Weather Forecasting

Learning to detect cloud and snow in remote sensing images from noisy labels

no code implementations17 Jan 2024 Zili Liu, Hao Chen, Wenyuan Li, Keyan Chen, Zipeng Qi, Chenyang Liu, Zhengxia Zou, Zhenwei Shi

This paper is the first to consider the impact of label noise on the detection of clouds and snow in remote sensing images.

Semantic Segmentation

Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method

no code implementations22 Jan 2024 Zili Liu, Hao Chen, Lei Bai, Wenyuan Li, Keyan Chen, Zhengyi Wang, Wanli Ouyang, Zhengxia Zou, Zhenwei Shi

In this paper, we extend meteorological downscaling to arbitrary scattered station scales, establish a brand new benchmark and dataset, and retrieve meteorological states at any given station location from a coarse-resolution meteorological field.

Super-Resolution Weather Forecasting

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