1 code implementation • ICCV 2023 • Mengmeng Jing, XianTong Zhen, Jingjing Li, Cees Snoek
To alleviate this problem, data augmentation coupled with consistency regularization are commonly adopted to make the model less sensitive to domain-specific attributes.
1 code implementation • NeurIPS 2023 • Yingjun Du, Zehao Xiao, Shengcai Liao, Cees Snoek
Furthermore, we introduce a task-guided diffusion process within the prototype space, enabling the meta-learning of a generative process that transitions from a vanilla prototype to an overfitted prototype.
2 code implementations • ICCV 2023 • Fida Mohammad Thoker, Hazel Doughty, Cees Snoek
By simulating different tubelet motions and applying transformations, such as scaling and rotation, we introduce motion patterns beyond what is present in the pretraining data.
1 code implementation • 27 Mar 2022 • Fida Mohammad Thoker, Hazel Doughty, Piyush Bagad, Cees Snoek
Despite the recent success of video self-supervised learning models, there is much still to be understood about their generalization capability.
1 code implementation • 19 Jul 2021 • Zenglin Shi, Pascal Mettes, Guoyan Zheng, Cees Snoek
In this paper, we find that all existing approaches share a common limitation: reconstruction breaks down in and around the high-frequency parts of CT images.
1 code implementation • ICML 2020 • Xiantong Zhen, Haoliang Sun, Ying-Jun Du, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek
We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable.
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.
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 • 21 Apr 2016 • Roeland De Geest, Efstratios Gavves, Amir Ghodrati, Zhenyang Li, Cees Snoek, Tinne Tuytelaars
Third, the start of the action is unknown, so it is unclear over what time window the information should be integrated.