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Found 8 papers, 4 papers with code
Image retrieval outperforms diffusion models on data augmentation
Many approaches have been proposed to use diffusion models to augment training datasets for downstream tasks, such as classification.
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions.
On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors
Predicting the states of dynamic traffic actors into the future is important for autonomous systems to operate safelyand efficiently.
Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior
In this paper, we investigate the problem of anticipating future dynamics, particularly the future location of other vehicles and pedestrians, in the view of a moving vehicle.
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
Future prediction is a fundamental principle of intelligence that helps plan actions and avoid possible dangers.
FusionNet and AugmentedFlowNet: Selective Proxy Ground Truth for Training on Unlabeled Images
The latter can be used as proxy-ground-truth to train a network on real-world data and to adapt it to specific domains of interest.
Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow
Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology.
End-to-End Learning of Video Super-Resolution with Motion Compensation
We analyze the usage of optical flow for video super-resolution and find that common off-the-shelf image warping does not allow video super-resolution to benefit much from optical flow.
