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Found 8 papers, 2 papers with code
ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation
To address this challenge, we propose ADAPT, a novel approach for jointly predicting the trajectories of all agents in the scene with dynamic weight learning.
Stochastic Future Prediction in Real World Driving Scenarios
Uncertainty plays a key role in future prediction.
Trajectory Forecasting on Temporal Graphs
We complement our representation with two types of memory modules; one focusing on the agent of interest and the other on the entire scene.
Ranked #51 on
Motion Forecasting
on Argoverse CVPR 2020
StretchBEV: Stretching Future Instance Prediction Spatially and Temporally
Our model learns temporal dynamics in a latent space through stochastic residual updates at each time step.
Stochastic Video Prediction with Structure and Motion
The existing methods fail to fully capture the dynamics of the structured world by only focusing on changes in pixels.
SLAMP: Stochastic Latent Appearance and Motion Prediction
Motion is an important cue for video prediction and often utilized by separating video content into static and dynamic components.
Ranked #1 on
Video Prediction
on Cityscapes 128x128
(PSNR metric)
Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation
The noise added to the original image is defined as the gradient of the cost function of the model.
Just Noticeable Difference for Machines to Generate Adversarial Images
We define Just Noticeable Difference for a machine learning model and generate a least perceptible difference for adversarial images which can trick a model.
