1 code implementation • 27 Dec 2022 • Abdullah Hamdi, Faisal AlZahrani, Silvio Giancola, Bernard Ghanem
Multi-view projection techniques have shown themselves to be highly effective in achieving top-performing results in the recognition of 3D shapes.
no code implementations • 14 Dec 2022 • Jinjie Mai, Abdullah Hamdi, Silvio Giancola, Chen Zhao, Bernard Ghanem
With the recent advances in video and 3D understanding, novel 4D spatio-temporal challenges fusing both concepts have emerged.
no code implementations • 21 Nov 2022 • Jesus Zarzar, Sara Rojas, Silvio Giancola, Bernard Ghanem
The predicted semantic fields allow SegNeRF to achieve an average mIoU of $\textbf{30. 30%}$ for 2D novel view segmentation, and $\textbf{37. 46%}$ for 3D part segmentation, boasting competitive performance against point-based methods by using only a few posed images.
no code implementations • 18 Nov 2022 • Jinjie Mai, Chen Zhao, Abdullah Hamdi, Silvio Giancola, Bernard Ghanem
Visual queries 3D localization (VQ3D) is a task in the Ego4D Episodic Memory Benchmark.
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 • 14 Apr 2022 • Anthony Cioppa, Silvio Giancola, Adrien Deliege, Le Kang, Xin Zhou, Zhiyu Cheng, Bernard Ghanem, Marc Van Droogenbroeck
Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation.
1 code implementation • CVPR 2022 • Gabriel Pérez S., Juan C. Pérez, Motasem Alfarra, Silvio Giancola, Bernard Ghanem
In this work, we propose 3DeformRS, a method to certify the robustness of point cloud Deep Neural Networks (DNNs) against real-world deformations.
1 code implementation • CVPR 2022 • Maksim Makarenko, Arturo Burguete-Lopez, Qizhou Wang, Fedor Getman, Silvio Giancola, Bernard Ghanem, Andrea Fratalocchi
Hyperspectral imaging has attracted significant attention to identify spectral signatures for image classification and automated pattern recognition in computer vision.
1 code implementation • CVPR 2022 • Mattia Soldan, Alejandro Pardo, Juan León Alcázar, Fabian Caba Heilbron, Chen Zhao, Silvio Giancola, Bernard Ghanem
The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques.
Ranked #1 on
Natural Language Moment Retrieval
on MAD
2 code implementations • 30 Nov 2021 • Abdullah Hamdi, Silvio Giancola, Bernard Ghanem
To this end, we introduce the concept of the multi-view point cloud (Voint cloud), representing each 3D point as a set of features extracted from several view-points.
1 code implementation • 10 May 2021 • Bing Li, Cheng Zheng, Silvio Giancola, Bernard Ghanem
We propose a novel scene flow estimation approach to capture and infer 3D motions from point clouds.
no code implementations • 19 Apr 2021 • Anthony Cioppa, Adrien Deliège, Floriane Magera, Silvio Giancola, Olivier Barnich, Bernard Ghanem, Marc Van Droogenbroeck
Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games.
1 code implementation • 14 Apr 2021 • Silvio Giancola, Bernard Ghanem
In this paper, we focus our analysis on action spotting in soccer broadcast, which consists in temporally localizing the main actions in a soccer game.
Ranked #1 on
Action Spotting
on SoccerNet-v2
no code implementations • 29 Dec 2020 • Hani Itani, Silvio Giancola, Ali Thabet, Bernard Ghanem
Since it is learnable, this mapping is allowed to be different per layer instead of being applied uniformly throughout the depth of the network.
2 code implementations • ICCV 2021 • Abdullah Hamdi, Silvio Giancola, Bernard Ghanem
MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.
Ranked #1 on
3D Object Retrieval
on ShapeNetCore 55
3 code implementations • 26 Nov 2020 • Adrien Deliège, Anthony Cioppa, Silvio Giancola, Meisam J. Seikavandi, Jacob V. Dueholm, Kamal Nasrollahi, Bernard Ghanem, Thomas B. Moeslund, Marc Van Droogenbroeck
In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production.
Ranked #1 on
Camera shot segmentation
on SoccerNet-v2
1 code implementation • 23 Nov 2020 • Humam Alwassel, Silvio Giancola, Bernard Ghanem
Extensive experiments show that using features trained with our novel pretraining strategy significantly improves the performance of recent state-of-the-art methods on three tasks: Temporal Action Localization, Action Proposal Generation, and Dense Video Captioning.
no code implementations • 24 Aug 2020 • Guohao Li, Mengmeng Xu, Silvio Giancola, Ali Thabet, Bernard Ghanem
In this paper, we introduce a new NAS framework, dubbed LC-NAS, where we search for point cloud architectures that are constrained to a target latency.
1 code implementation • CVPR 2020 • Anthony Cioppa, Adrien Deliège, Silvio Giancola, Bernard Ghanem, Marc Van Droogenbroeck, Rikke Gade, Thomas B. Moeslund
We benchmark our loss on a large dataset of soccer videos, SoccerNet, and achieve an improvement of 12. 8% over the baseline.
Ranked #2 on
Action Spotting
on SoccerNet-v2
no code implementations • 27 Nov 2019 • Jesus Zarzar, Silvio Giancola, Bernard Ghanem
We integrate residual GCNs in a two-stage 3D object detection pipeline, where 3D object proposals are refined using a novel graph representation.
Ranked #14 on
3D Object Detection
on KITTI Cars Hard
no code implementations • 25 Mar 2019 • Jesus Zarzar, Silvio Giancola, Bernard Ghanem
Successively, we refine our selection of 3D object candidates by exploiting the similarity capability of a 3D Siamese network.
1 code implementation • CVPR 2019 • Silvio Giancola, Jesus Zarzar, Bernard Ghanem
We design a Siamese tracker that encodes model and candidate shapes into a compact latent representation.
2 code implementations • 12 Apr 2018 • Silvio Giancola, Mohieddine Amine, Tarek Dghaily, Bernard Ghanem
A total of 6, 637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution).
Ranked #6 on
Action Spotting
on SoccerNet
1 code implementation • ECCV 2018 • Matthias Müller, Adel Bibi, Silvio Giancola, Salman Al-Subaihi, Bernard Ghanem
In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild.
no code implementations • 12 Feb 2018 • Silvio Giancola, Jens Schneider, Peter Wonka, Bernard S. Ghanem
We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances.
no code implementations • 7 Aug 2017 • Silvio Giancola, Daniele Piron, Pasquale Poppa, Remo Sala
In this work, we propose a method for three-dimensional (3D) reconstruction of wide crime scene, based on a Simultaneous Localization and Mapping (SLAM) approach.