419 research outputs found
Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network
In this paper we present a novel instance segmentation algorithm that extends a fully convolutional network to learn to label objects separately without prediction of regions of interest. We trained the new algorithm on a challenging CCTV recording of beef cattle, as well as benchmark MS COCO and Pascal VOC datasets. Extensive experimentation showed that our approach outperforms the state-of-the-art solutions by up to 8% on our data
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
From Facial Parts Responses to Face Detection: A Deep Learning Approach
In this paper, we propose a novel deep convolutional network (DCN) that
achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically,
our method achieves a high recall rate of 90.99% on the challenging FDDB
benchmark, outperforming the state-of-the-art method by a large margin of
2.91%. Importantly, we consider finding faces from a new perspective through
scoring facial parts responses by their spatial structure and arrangement. The
scoring mechanism is carefully formulated considering challenging cases where
faces are only partially visible. This consideration allows our network to
detect faces under severe occlusion and unconstrained pose variation, which are
the main difficulty and bottleneck of most existing face detection approaches.
We show that despite the use of DCN, our network can achieve practical runtime
speed.Comment: To appear in ICCV 201
Pixelwise Instance Segmentation with a Dynamically Instantiated Network
Semantic segmentation and object detection research have recently achieved
rapid progress. However, the former task has no notion of different instances
of the same object, and the latter operates at a coarse, bounding-box level. We
propose an Instance Segmentation system that produces a segmentation map where
each pixel is assigned an object class and instance identity label. Most
approaches adapt object detectors to produce segments instead of boxes. In
contrast, our method is based on an initial semantic segmentation module, which
feeds into an instance subnetwork. This subnetwork uses the initial
category-level segmentation, along with cues from the output of an object
detector, within an end-to-end CRF to predict instances. This part of our model
is dynamically instantiated to produce a variable number of instances per
image. Our end-to-end approach requires no post-processing and considers the
image holistically, instead of processing independent proposals. Therefore,
unlike some related work, a pixel cannot belong to multiple instances.
Furthermore, far more precise segmentations are achieved, as shown by our
state-of-the-art results (particularly at high IoU thresholds) on the Pascal
VOC and Cityscapes datasets.Comment: CVPR 201
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