1 code implementation • ECCV 2020 • Junwei Liang, Lu Jiang, Alexander Hauptmann
We approach this problem through the real-data-free setting in which the model is trained only on 3D simulation data and applied out-of-the-box to a wide variety of real cameras.
Ranked #1 on
Trajectory Forecasting
on ActEV
6 code implementations • 5 Oct 2022 • Silvio Giancola, Anthony Cioppa, Adrien Deliège, Floriane Magera, Vladimir Somers, Le Kang, Xin Zhou, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdulrahman Darwish, Adrien Maglo, Albert Clapés, Andreas Luyts, Andrei Boiarov, Artur Xarles, Astrid Orcesi, Avijit Shah, Baoyu Fan, Bharath Comandur, Chen Chen, Chen Zhang, Chen Zhao, Chengzhi Lin, Cheuk-Yiu Chan, Chun Chuen Hui, Dengjie Li, Fan Yang, Fan Liang, Fang Da, Feng Yan, Fufu Yu, Guanshuo Wang, H. Anthony Chan, He Zhu, Hongwei Kan, Jiaming Chu, Jianming Hu, Jianyang Gu, Jin Chen, João V. B. Soares, Jonas Theiner, Jorge De Corte, José Henrique Brito, Jun Zhang, Junjie Li, Junwei Liang, Leqi Shen, Lin Ma, Lingchi Chen, Miguel Santos Marques, Mike Azatov, Nikita Kasatkin, Ning Wang, Qiong Jia, Quoc Cuong Pham, Ralph Ewerth, Ran Song, RenGang Li, Rikke Gade, Ruben Debien, Runze Zhang, Sangrok Lee, Sergio Escalera, Shan Jiang, Shigeyuki Odashima, Shimin Chen, Shoichi Masui, Shouhong Ding, Sin-wai Chan, Siyu Chen, Tallal El-Shabrawy, Tao He, Thomas B. Moeslund, Wan-Chi Siu, Wei zhang, Wei Li, Xiangwei Wang, Xiao Tan, Xiaochuan Li, Xiaolin Wei, Xiaoqing Ye, Xing Liu, Xinying Wang, Yandong Guo, YaQian Zhao, Yi Yu, YingYing Li, Yue He, Yujie Zhong, Zhenhua Guo, Zhiheng Li
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
no code implementations • 27 Sep 2022 • Chengzhi Lin, AnCong Wu, Junwei Liang, Jun Zhang, Wenhang Ge, Wei-Shi Zheng, Chunhua Shen
To address this problem, we propose a Text-Adaptive Multiple Visual Prototype Matching model, which automatically captures multiple prototypes to describe a video by adaptive aggregation of video token features.
1 code implementation • 26 Sep 2022 • Junwei Liang, Enwei Zhang, Jun Zhang, Chunhua Shen
We study the task of robust feature representations, aiming to generalize well on multiple datasets for action recognition.
1 code implementation • IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022 • Junwei Liang, He Zhu, Enwei Zhang, Jun Zhang
Distracted driver actions can be dangerous and cause severe accidents.
no code implementations • ICCV 2021 • Xiaoyu Zhu, Jeffrey Chen, Xiangrui Zeng, Junwei Liang, Chengqi Li, Sinuo Liu, Sima Behpour, Min Xu
We propose a novel weakly supervised approach for 3D semantic segmentation on volumetric images.
no code implementations • 4 Dec 2020 • Junwei Liang, Liangliang Cao, Xuehan Xiong, Ting Yu, Alexander Hauptmann
The experimental results show that the STAN model can consistently improve the state of the arts in both action detection and action recognition tasks.
4 code implementations • 20 Nov 2020 • Junwei Liang
With the advancement in computer vision deep learning, systems now are able to analyze an unprecedented amount of rich visual information from videos to enable applications such as autonomous driving, socially-aware robot assistant and public safety monitoring.
1 code implementation • 30 Jun 2020 • Xiaoyu Zhu, Junwei Liang, Alexander Hauptmann
This provides the first benchmark for quantitative evaluation of models to assess building damage using aerial videos.
1 code implementation • 4 Apr 2020 • Junwei Liang, Lu Jiang, Alexander Hauptmann
We refer to our method as SimAug.
Ranked #1 on
Trajectory Prediction
on ActEV
1 code implementation • Proceedings of the IEEE Winter Conference on Applications of Computer Vision Workshops 2020 • Wenhe Liu, Guoliang Kang, Po-Yao Huang, Xiaojun Chang, Yijun Qian, Junwei Liang, Liangke Gui, Jing Wen, Peng Chen
We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario.
1 code implementation • CVPR 2020 • Junwei Liang, Lu Jiang, Kevin Murphy, Ting Yu, Alexander Hauptmann
The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals.
Ranked #1 on
Multi-future Trajectory Prediction
on ForkingPaths
2 code implementations • 26 May 2019 • Junwei Liang, Jay D. Aronson, Alexander Hauptmann
Among other uses, VERA enables the localization of a shooter from just a few videos that include the sound of gunshots.
2 code implementations • CVPR 2019 • Junwei Liang, Lu Jiang, Juan Carlos Niebles, Alexander Hauptmann, Li Fei-Fei
To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.
Ranked #1 on
Activity Prediction
on ActEV
1 code implementation • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2018 • Junwei Liang, Lu Jiang, Liangliang Cao, Yannis Kalantidis, Li-Jia Li, and Alexander Hauptmann
In addition to a text answer, a few grounding photos are also given to justify the answer.
Ranked #1 on
Memex Question Answering
on MemexQA
2 code implementations • CVPR 2018 • Junwei Liang, Lu Jiang, Liangliang Cao, Li-Jia Li, Alexander Hauptmann
Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering.
Ranked #1 on
Memex Question Answering
on MemexQA
1 code implementation • 4 Aug 2017 • Lu Jiang, Junwei Liang, Liangliang Cao, Yannis Kalantidis, Sachin Farfade, Alexander Hauptmann
This paper proposes a new task, MemexQA: given a collection of photos or videos from a user, the goal is to automatically answer questions that help users recover their memory about events captured in the collection.
1 code implementation • 16 Jul 2016 • Junwei Liang, Lu Jiang, Deyu Meng, Alexander Hauptmann
Learning video concept detectors automatically from the big but noisy web data with no additional manual annotations is a novel but challenging area in the multimedia and the machine learning community.