3,115 research outputs found
The Secrets of Salient Object Segmentation
In this paper we provide an extensive evaluation of fixation prediction and
salient object segmentation algorithms as well as statistics of major datasets.
Our analysis identifies serious design flaws of existing salient object
benchmarks, called the dataset design bias, by over emphasizing the
stereotypical concepts of saliency. The dataset design bias does not only
create the discomforting disconnection between fixations and salient object
segmentation, but also misleads the algorithm designing. Based on our analysis,
we propose a new high quality dataset that offers both fixation and salient
object segmentation ground-truth. With fixations and salient object being
presented simultaneously, we are able to bridge the gap between fixations and
salient objects, and propose a novel method for salient object segmentation.
Finally, we report significant benchmark progress on three existing datasets of
segmenting salient objectsComment: 15 pages, 8 figures. Conference version was accepted by CVPR 201
Integrated Deep and Shallow Networks for Salient Object Detection
Deep convolutional neural network (CNN) based salient object detection
methods have achieved state-of-the-art performance and outperform those
unsupervised methods with a wide margin. In this paper, we propose to integrate
deep and unsupervised saliency for salient object detection under a unified
framework. Specifically, our method takes results of unsupervised saliency
(Robust Background Detection, RBD) and normalized color images as inputs, and
directly learns an end-to-end mapping between inputs and the corresponding
saliency maps. The color images are fed into a Fully Convolutional Neural
Networks (FCNN) adapted from semantic segmentation to exploit high-level
semantic cues for salient object detection. Then the results from deep FCNN and
RBD are concatenated to feed into a shallow network to map the concatenated
feature maps to saliency maps. Finally, to obtain a spatially consistent
saliency map with sharp object boundaries, we fuse superpixel level saliency
map at multi-scale. Extensive experimental results on 8 benchmark datasets
demonstrate that the proposed method outperforms the state-of-the-art
approaches with a margin.Comment: Accepted by IEEE International Conference on Image Processing (ICIP)
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
Instance-Level Salient Object Segmentation
Image saliency detection has recently witnessed rapid progress due to deep
convolutional neural networks. However, none of the existing methods is able to
identify object instances in the detected salient regions. In this paper, we
present a salient instance segmentation method that produces a saliency mask
with distinct object instance labels for an input image. Our method consists of
three steps, estimating saliency map, detecting salient object contours and
identifying salient object instances. For the first two steps, we propose a
multiscale saliency refinement network, which generates high-quality salient
region masks and salient object contours. Once integrated with multiscale
combinatorial grouping and a MAP-based subset optimization framework, our
method can generate very promising salient object instance segmentation
results. To promote further research and evaluation of salient instance
segmentation, we also construct a new database of 1000 images and their
pixelwise salient instance annotations. Experimental results demonstrate that
our proposed method is capable of achieving state-of-the-art performance on all
public benchmarks for salient region detection as well as on our new dataset
for salient instance segmentation.Comment: To appear in CVPR201
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
We propose an approach to discover class-specific pixels for the
weakly-supervised semantic segmentation task. We show that properly combining
saliency and attention maps allows us to obtain reliable cues capable of
significantly boosting the performance. First, we propose a simple yet powerful
hierarchical approach to discover the class-agnostic salient regions, obtained
using a salient object detector, which otherwise would be ignored. Second, we
use fully convolutional attention maps to reliably localize the class-specific
regions in a given image. We combine these two cues to discover class-specific
pixels which are then used as an approximate ground truth for training a CNN.
While solving the weakly supervised semantic segmentation task, we ensure that
the image-level classification task is also solved in order to enforce the CNN
to assign at least one pixel to each object present in the image.
Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of
60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to
the published state-of-the-art results. The code is made publicly available
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