6,483 research outputs found
Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground
We provide a comprehensive evaluation of salient object detection (SOD)
models. Our analysis identifies a serious design bias of existing SOD datasets
which assumes that each image contains at least one clearly outstanding salient
object in low clutter. The design bias has led to a saturated high performance
for state-of-the-art SOD models when evaluated on existing datasets. The
models, however, still perform far from being satisfactory when applied to
real-world daily scenes. Based on our analyses, we first identify 7 crucial
aspects that a comprehensive and balanced dataset should fulfill. Then, we
propose a new high quality dataset and update the previous saliency benchmark.
Specifically, our SOC (Salient Objects in Clutter) dataset, includes images
with salient and non-salient objects from daily object categories. Beyond
object category annotations, each salient image is accompanied by attributes
that reflect common challenges in real-world scenes. Finally, we report
attribute-based performance assessment on our dataset.Comment: ECCV 201
Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels
Computer vision algorithms with pixel-wise labeling tasks, such as semantic
segmentation and salient object detection, have gone through a significant
accuracy increase with the incorporation of deep learning. Deep segmentation
methods slightly modify and fine-tune pre-trained networks that have hundreds
of millions of parameters. In this work, we question the need to have such
memory demanding networks for the specific task of salient object segmentation.
To this end, we propose a way to learn a memory-efficient network from scratch
by training it only on salient object detection datasets. Our method encodes
images to gridized superpixels that preserve both the object boundaries and the
connectivity rules of regular pixels. This representation allows us to use
convolutional neural networks that operate on regular grids. By using these
encoded images, we train a memory-efficient network using only 0.048\% of the
number of parameters that other deep salient object detection networks have.
Our method shows comparable accuracy with the state-of-the-art deep salient
object detection methods and provides a faster and a much more memory-efficient
alternative to them. Due to its easy deployment, such a network is preferable
for applications in memory limited devices such as mobile phones and IoT
devices.Comment: 6 pages, submitted to MMSP 201
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