Search Results for author: Javen Shi

Found 8 papers, 1 papers with code

Learning for Visual Navigation by Imagining the Success

no code implementations28 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.

Visual Navigation

Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos

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.

Group Activity Recognition

MOT20: A benchmark for multi object tracking in crowded scenes

1 code implementation19 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 +1

Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Langauge Reasoning

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.

Visual Dialog

What's to know? Uncertainty as a Guide to Asking Goal-oriented Questions

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.

Information Seeking Visual Dialog

Deep Lipschitz networks and Dudley GANs

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.

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