Steering Prediction via a Multi-Sensor System for Autonomous Racing

no code implementations • 28 Sep 2024 • Zhuyun Zhou, Zongwei Wu, Florian Bolli, Rémi Boutteau, Fan Yang, Radu Timofte, Dominique Ginhac, Tobi Delbruck

Our goal is to fuse the 2D LiDAR data with event data in an end-to-end learning framework for steering prediction, which is crucial for autonomous racing.

Autonomous Racing

Alignment-free HDR Deghosting with Semantics Consistent Transformer

no code implementations • ICCV 2023 • Steven Tel, Zongwei Wu, Yulun Zhang, Barthélémy Heyrman, Cédric Demonceaux, Radu Timofte, Dominique Ginhac

The spatial attention aims to deal with the intra-image correlation to model the dynamic motion, while the channel attention enables the inter-image intertwining to enhance the semantic consistency across frames.

Image Generation

Event-Free Moving Object Segmentation from Moving Ego Vehicle

2 code implementations • 28 Apr 2023 • Zhuyun Zhou, Zongwei Wu, Danda Pani Paudel, Rémi Boutteau, Fan Yang, Luc van Gool, Radu Timofte, Dominique Ginhac

Subsequently, we devise EmoFormer, a novel network able to exploit the event data.

Autonomous Driving Benchmarking +7

CEN-HDR: Computationally Efficient neural Network for real-time High Dynamic Range imaging

1 code implementation • 10 Feb 2023 • Steven Tel, Barthélémy Heyrman, Dominique Ginhac

High dynamic range (HDR) imaging is still a challenging task in modern digital photography.

Efficient Neural Network Knowledge Distillation

RGB-Event Fusion for Moving Object Detection in Autonomous Driving

2 code implementations • 17 Sep 2022 • Zhuyun Zhou, Zongwei Wu, Rémi Boutteau, Fan Yang, Cédric Demonceaux, Dominique Ginhac

Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving.

Autonomous Driving Moving Object Detection +1

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations • 25 May 2022 • Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration Vocal Bursts Intensity Prediction

Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

no code implementations • 9 Aug 2021 • Alain Lalande, Zhihao Chen, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum, Michel Salomon, Dominique Ginhac, Youssef Skandarani, Arnaud Boucher, Khawla Brahim, Marleen de Bruijne, Robin Camarasa, Teresa M. Correia, Xue Feng, Kibrom B. Girum, Anja Hennemuth, Markus Huellebrand, Raabid Hussain, Matthias Ivantsits, Jun Ma, Craig Meyer, Rishabh Sharma, Jixi Shi, Nikolaos V. Tsekos, Marta Varela, Xiyue Wang, Sen yang, Hannu Zhang, Yichi Zhang, Yuncheng Zhou, Xiahai Zhuang, Raphael Couturier, Fabrice Meriaudeau

The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department.

ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation

1 code implementation • 26 Feb 2021 • Yu Liu, Fan Yang, Dominique Ginhac

Interpreting human actions requires understanding the spatial and temporal context of the scenes.

Action Detection Edge-computing