6,632 research outputs found
CAR-Net: Clairvoyant Attentive Recurrent Network
We present an interpretable framework for path prediction that leverages
dependencies between agents' behaviors and their spatial navigation
environment. We exploit two sources of information: the past motion trajectory
of the agent of interest and a wide top-view image of the navigation scene. We
propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where
to look in a large image of the scene when solving the path prediction task.
Our method can attend to any area, or combination of areas, within the raw
image (e.g., road intersections) when predicting the trajectory of the agent.
This allows us to visualize fine-grained semantic elements of navigation scenes
that influence the prediction of trajectories. To study the impact of space on
agents' trajectories, we build a new dataset made of top-view images of
hundreds of scenes (Formula One racing tracks) where agents' behaviors are
heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net
successfully attends to these salient regions. Additionally, CAR-Net reaches
state-of-the-art accuracy on the standard trajectory forecasting benchmark,
Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize
to unseen scenes.Comment: The 2nd and 3rd authors contributed equall
Predicting Future Instance Segmentation by Forecasting Convolutional Features
Anticipating future events is an important prerequisite towards intelligent
behavior. Video forecasting has been studied as a proxy task towards this goal.
Recent work has shown that to predict semantic segmentation of future frames,
forecasting at the semantic level is more effective than forecasting RGB frames
and then segmenting these. In this paper we consider the more challenging
problem of future instance segmentation, which additionally segments out
individual objects. To deal with a varying number of output labels per image,
we develop a predictive model in the space of fixed-sized convolutional
features of the Mask R-CNN instance segmentation model. We apply the "detection
head'" of Mask R-CNN on the predicted features to produce the instance
segmentation of future frames. Experiments show that this approach
significantly improves over strong baselines based on optical flow and
repurposed instance segmentation architectures
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