2,662 research outputs found
GFF: Gated Fully Fusion for Semantic Segmentation
Semantic segmentation generates comprehensive understanding of scenes through
densely predicting the category for each pixel. High-level features from Deep
Convolutional Neural Networks already demonstrate their effectiveness in
semantic segmentation tasks, however the coarse resolution of high-level
features often leads to inferior results for small/thin objects where detailed
information is important. It is natural to consider importing low level
features to compensate for the lost detailed information in high-level
features.Unfortunately, simply combining multi-level features suffers from the
semantic gap among them. In this paper, we propose a new architecture, named
Gated Fully Fusion (GFF), to selectively fuse features from multiple levels
using gates in a fully connected way. Specifically, features at each level are
enhanced by higher-level features with stronger semantics and lower-level
features with more details, and gates are used to control the propagation of
useful information which significantly reduces the noises during fusion. We
achieve the state of the art results on four challenging scene parsing datasets
including Cityscapes, Pascal Context, COCO-stuff and ADE20K.Comment: accepted by AAAI-2020(oral
Recurrent Scene Parsing with Perspective Understanding in the Loop
Objects may appear at arbitrary scales in perspective images of a scene,
posing a challenge for recognition systems that process images at a fixed
resolution. We propose a depth-aware gating module that adaptively selects the
pooling field size in a convolutional network architecture according to the
object scale (inversely proportional to the depth) so that small details are
preserved for distant objects while larger receptive fields are used for those
nearby. The depth gating signal is provided by stereo disparity or estimated
directly from monocular input. We integrate this depth-aware gating into a
recurrent convolutional neural network to perform semantic segmentation. Our
recurrent module iteratively refines the segmentation results, leveraging the
depth and semantic predictions from the previous iterations.
Through extensive experiments on four popular large-scale RGB-D datasets, we
demonstrate this approach achieves competitive semantic segmentation
performance with a model which is substantially more compact. We carry out
extensive analysis of this architecture including variants that operate on
monocular RGB but use depth as side-information during training, unsupervised
gating as a generic attentional mechanism, and multi-resolution gating. We find
that gated pooling for joint semantic segmentation and depth yields
state-of-the-art results for quantitative monocular depth estimation
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