Search Results for author:
Found 8 papers, 1 papers with code
Learning for Visual Navigation by Imagining the Success
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.
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos
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.
MOT20: A benchmark for multi object tracking in crowded scenes
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
CVPR19 Tracking and Detection Challenge: How crowded can it get?
Standardized benchmarks are crucial for the majority of computer vision applications.
Follow the Attention: Combining Partial Pose and Object Motion for Fine-Grained Action Detection
We empirically show the capability of our approach by achieving state-of-the-art results on MERL shopping dataset.
What's to know? Uncertainty as a Guide to Asking Goal-oriented Questions
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.
Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Langauge Reasoning
For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed.
Deep Lipschitz networks and Dudley GANs
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.
