16,135 research outputs found
Predicting Deeper into the Future of Semantic Segmentation
The ability to predict and therefore to anticipate the future is an important
attribute of intelligence. It is also of utmost importance in real-time
systems, e.g. in robotics or autonomous driving, which depend on visual scene
understanding for decision making. While prediction of the raw RGB pixel values
in future video frames has been studied in previous work, here we introduce the
novel task of predicting semantic segmentations of future frames. Given a
sequence of video frames, our goal is to predict segmentation maps of not yet
observed video frames that lie up to a second or further in the future. We
develop an autoregressive convolutional neural network that learns to
iteratively generate multiple frames. Our results on the Cityscapes dataset
show that directly predicting future segmentations is substantially better than
predicting and then segmenting future RGB frames. Prediction results up to half
a second in the future are visually convincing and are much more accurate than
those of a baseline based on warping semantic segmentations using optical flow.Comment: Accepted to ICCV 2017. Supplementary material available on the
authors' webpage
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
Recurrent Segmentation for Variable Computational Budgets
State-of-the-art systems for semantic image segmentation use feed-forward
pipelines with fixed computational costs. Building an image segmentation system
that works across a range of computational budgets is challenging and
time-intensive as new architectures must be designed and trained for every
computational setting. To address this problem we develop a recurrent neural
network that successively improves prediction quality with each iteration.
Importantly, the RNN may be deployed across a range of computational budgets by
merely running the model for a variable number of iterations. We find that this
architecture is uniquely suited for efficiently segmenting videos. By
exploiting the segmentation of past frames, the RNN can perform video
segmentation at similar quality but reduced computational cost compared to
state-of-the-art image segmentation methods. When applied to static images in
the PASCAL VOC 2012 and Cityscapes segmentation datasets, the RNN traces out a
speed-accuracy curve that saturates near the performance of state-of-the-art
segmentation methods
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