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Found 12 papers, 7 papers with code
Predicting the Driver's Focus of Attention: the DR(eye)VE Project
In this work we aim to predict the driver's focus of attention.
TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation
We address unsupervised optical flow estimation for ego-centric motion.
Learning to Map Vehicles into Bird's Eye View
Awareness of the road scene is an essential component for both autonomous vehicles and Advances Driver Assistance Systems and is gaining importance both for the academia and car companies.
Latent Space Autoregression for Novelty Detection
Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity.
Classifying Signals on Irregular Domains via Convolutional Cluster Pooling
We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset.
Conditional Channel Gated Networks for Task-Aware Continual Learning
Therefore, we additionally introduce a task classifier that predicts the task label of each example, to deal with settings in which a task oracle is not available.
Ranked #3 on
Continual Learning
on ImageNet-50 (5 tasks)
Dark Experience for General Continual Learning: a Strong, Simple Baseline
Continual Learning has inspired a plethora of approaches and evaluation settings; however, the majority of them overlooks the properties of a practical scenario, where the data stream cannot be shaped as a sequence of tasks and offline training is not viable.
Ranked #12 on
Continual Learning
on ASC (19 tasks)
Skip-Convolutions for Efficient Video Processing
We reformulate standard convolution to be efficiently computed on residual frames: each layer is coupled with a binary gate deciding whether a residual is important to the model prediction,~\eg foreground regions, or it can be safely skipped, e. g. background regions.
Region-of-Interest Based Neural Video Compression
To the best of our knowledge, our proposals are the first solutions that integrate ROI-based capabilities into neural video compression models.
Delta Distillation for Efficient Video Processing
By extensive experiments on a wide range of architectures, including the most efficient ones, we demonstrate that delta distillation sets a new state of the art in terms of accuracy vs. efficiency trade-off for semantic segmentation and object detection in videos.
Ranked #2 on
Video Semantic Segmentation
on Cityscapes val
ResQ: Residual Quantization for Video Perception
Furthermore, we extend our model to dynamically adjust the bit-width proportional to the amount of changes in the video.
Object-Centric Diffusion for Efficient Video Editing
Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual edit prompts.
