no code implementations • 28 Feb 2021 • Mahdi Kazemi Moghaddam, Ehsan Abbasnejad, Qi Wu, Javen Shi, Anton Van Den Hengel
ForeSIT is trained to imagine the recurrent latent representation of a future state that leads to success, e. g. either a sub-goal state that is important to reach before the target, or the goal state itself.
no code implementations • ECCV 2020 • Mahsa Ehsanpour, Alireza Abedin, Fatemeh Saleh, Javen Shi, Ian Reid, Hamid Rezatofighi
In this paper, we solve the problem of simultaneously grouping people by their social interactions, predicting their individual actions and the social activity of each social group, which we call the social task.
1 code implementation • 19 Mar 2020 • Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixé
The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods.
Multi-Object Tracking Multiple Object Tracking with Transformer +2
no code implementations • 10 Jun 2019 • Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixe
Standardized benchmarks are crucial for the majority of computer vision applications.
no code implementations • 11 May 2019 • Mohammad Mahdi Kazemi Moghaddam, Ehsan Abbasnejad, Javen Shi
We empirically show the capability of our approach by achieving state-of-the-art results on MERL shopping dataset.
no code implementations • CVPR 2019 • Ehsan Abbasnejad, Qi Wu, Javen Shi, Anton Van Den Hengel
We propose a solution to this problem based on a Bayesian model of the uncertainty in the implicit model maintained by the visual dialogue agent, and in the function used to select an appropriate output.
no code implementations • CVPR 2020 • Ehsan Abbasnejad, Iman Abbasnejad, Qi Wu, Javen Shi, Anton Van Den Hengel
For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed.
no code implementations • ICLR 2018 • Ehsan Abbasnejad, Javen Shi, Anton Van Den Hengel
To facilitate this, we develop both theoretical and practical building blocks, using which one can construct different neural networks using a large range of metrics, as well as ensure Lipschitz condition and sufficient capacity of the networks.