no code implementations • 10 Apr 2022 • Victor Escorcia, Ricardo Guerrero, Xiatian Zhu, Brais Martinez
To overcome both limitations, we introduce Self-Supervised Learning Over Sets (SOS), an approach to pre-train a generic Objects In Contact (OIC) representation model from video object regions detected by an off-the-shelf hand-object contact detector.
no code implementations • 10 Feb 2022 • Merey Ramazanova, Victor Escorcia, Fabian Caba Heilbron, Chen Zhao, Bernard Ghanem
We validate our approach in two large-scale datasets, EPIC-Kitchens, and HOMAGE.
no code implementations • CVPR 2022 • Andrés Villa, Kumail Alhamoud, Juan León Alcázar, Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem
We perform in-depth evaluations of existing CL methods in vCLIMB, and observe two unique challenges in video data.
2 code implementations • 21 Jun 2021 • Andrés Villa, Juan-Manuel Perez-Rua, Vladimir Araujo, Juan Carlos Niebles, Victor Escorcia, Alvaro Soto
Recently, few-shot learning has received increasing interest.
1 code implementation • ICCV 2021 • Mengmeng Xu, Juan-Manuel Perez-Rua, Victor Escorcia, Brais Martinez, Xiatian Zhu, Li Zhang, Bernard Ghanem, Tao Xiang
However, most existing models developed for these tasks are pre-trained on general video action classification tasks.
Ranked #23 on
Temporal Action Localization
on ActivityNet-1.3
no code implementations • 3 Jul 2020 • Juan-Manuel Perez-Rua, Antoine Toisoul, Brais Martinez, Victor Escorcia, Li Zhang, Xiatian Zhu, Tao Xiang
In this challenge, action recognition is posed as the problem of simultaneously predicting a single `verb' and `noun' class label given an input trimmed video clip.
no code implementations • 2 Apr 2020 • Juan-Manuel Perez-Rua, Brais Martinez, Xiatian Zhu, Antoine Toisoul, Victor Escorcia, Tao Xiang
Departing from existing alternatives, our W3 module models all three facets of video attention jointly.
Ranked #1 on
Action Recognition
on EgoGesture
2 code implementations • 30 Jul 2019 • Victor Escorcia, Mattia Soldan, Josef Sivic, Bernard Ghanem, Bryan Russell
We evaluate our approach on two recently proposed datasets for temporal localization of moments in video with natural language (DiDeMo and Charades-STA) extended to our video corpus moment retrieval setting.
no code implementations • 11 Aug 2018 • Bernard Ghanem, Juan Carlos Niebles, Cees Snoek, Fabian Caba Heilbron, Humam Alwassel, Victor Escorcia, Ranjay Krishna, Shyamal Buch, Cuong Duc Dao
The guest tasks focused on complementary aspects of the activity recognition problem at large scale and involved three challenging and recently compiled datasets: the Kinetics-600 dataset from Google DeepMind, the AVA dataset from Berkeley and Google, and the Moments in Time dataset from MIT and IBM Research.
1 code implementation • ECCV 2018 • Humam Alwassel, Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem
Despite the recent progress in video understanding and the continuous rate of improvement in temporal action localization throughout the years, it is still unclear how far (or close?)
2 code implementations • 5 Apr 2018 • Victor Escorcia, Cuong D. Dao, Mihir Jain, Bernard Ghanem, Cees Snoek
Second, we propose an actor-based attention mechanism that enables the localization of the actions from action class labels and actor proposals and is end-to-end trainable.
no code implementations • 22 Oct 2017 • Bernard Ghanem, Juan Carlos Niebles, Cees Snoek, Fabian Caba Heilbron, Humam Alwassel, Ranjay Khrisna, Victor Escorcia, Kenji Hata, Shyamal Buch
The ActivityNet Large Scale Activity Recognition Challenge 2017 Summary: results and challenge participants papers.
no code implementations • CVPR 2017 • Fabian Caba Heilbron, Wayner Barrios, Victor Escorcia, Bernard Ghanem
Despite the recent advances in large-scale video analysis, action detection remains as one of the most challenging unsolved problems in computer vision.
1 code implementation • CVPR 2017 • Shyamal Buch, Victor Escorcia, Chuanqi Shen, Bernard Ghanem, Juan Carlos Niebles
Our paper presents a new approach for temporal detection of human actions in long, untrimmed video sequences.
1 code implementation • CVPR 2015 • Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, Juan Carlos Niebles
In spite of many dataset efforts for human action recognition, current computer vision algorithms are still severely limited in terms of the variability and complexity of the actions that they can recognize.
no code implementations • CVPR 2015 • Victor Escorcia, Juan Carlos Niebles, Bernard Ghanem
One of the cornerstone principles of deep models is their abstraction capacity, i. e. their ability to learn abstract concepts from `simpler' ones.