1 code implementation • 18 Apr 2024 • Haoyue Liu, Shihan Peng, Lin Zhu, Yi Chang, Hanyu Zhou, Luxin Yan
In this work, we present a novel nighttime dynamic imaging method with an event camera.
no code implementations • 12 Mar 2024 • Hanyu Zhou, Zhiwei Shi, Hao Dong, Shihan Peng, Yi Chang, Luxin Yan
In spatial reasoning stage, we project the compensated events into the same image coordinate, discretize the timestamp of events to obtain a time image that can reflect the motion confidence, and further segment the moving object through adaptive threshold on the time image.
no code implementations • 12 Mar 2024 • Hanyu Zhou, Yi Chang, Zhiwei Shi, Luxin Yan
In this work, we bring the event as a bridge between RGB and LiDAR, and propose a novel hierarchical visual-motion fusion framework for scene flow, which explores a homogeneous space to fuse the cross-modal complementary knowledge for physical interpretation.
1 code implementation • 4 Mar 2024 • Xueqing Wu, Rui Zheng, Jingzhen Sha, Te-Lin Wu, Hanyu Zhou, Mohan Tang, Kai-Wei Chang, Nanyun Peng, Haoran Huang
We construct the DACO dataset, containing (1) 440 databases (of tabular data) collected from real-world scenarios, (2) ~2k query-answer pairs that can serve as weak supervision for model training, and (3) a concentrated but high-quality test set with human refined annotations that serves as our main evaluation benchmark.
no code implementations • 31 Jan 2024 • Hanyu Zhou, Yi Chang, Haoyue Liu, Wending Yan, Yuxing Duan, Zhiwei Shi, Luxin Yan
In appearance adaptation, we employ the intrinsic image decomposition to embed the auxiliary daytime image and the nighttime image into a reflectance-aligned common space.
1 code implementation • 6 Dec 2023 • Jialong Zuo, Hanyu Zhou, Ying Nie, Feng Zhang, Tianyu Guo, Nong Sang, Yunhe Wang, Changxin Gao
Firstly, we construct a new \textbf{dataset} named UFine6926.
1 code implementation • 10 Oct 2023 • Yu Zhou, Yunqiu Han, Hanyu Zhou, Yulun Wu
In this work, we aim to bridge the gap by introducing current ML-based methods to improve general purpose pre-trained language models in the task of commonsense reasoning.
no code implementations • 24 Mar 2023 • Hanyu Zhou, Yi Chang, Gang Chen, Luxin Yan
In motion adaptation, we utilize the flow consistency knowledge to align the cross-domain optical flows into a motion-invariance common space, where the optical flow from clean weather is used as the guidance-knowledge to obtain a preliminary optical flow for adverse weather.
no code implementations • CVPR 2023 • Hanyu Zhou, Yi Chang, Wending Yan, Luxin Yan
To handle the practical optical flow under real foggy scenes, in this work, we propose a novel unsupervised cumulative domain adaptation optical flow (UCDA-Flow) framework: depth-association motion adaptation and correlation-alignment motion adaptation.
no code implementations • CVPR 2021 • Yuntong Ye, Yi Chang, Hanyu Zhou, Luxin Yan
Existing deep learning-based image deraining methods have achieved promising performance for synthetic rainy images, typically rely on the pairs of sharp images and simulated rainy counterparts.