1,751 research outputs found
Reversible Recursive Instance-level Object Segmentation
In this work, we propose a novel Reversible Recursive Instance-level Object
Segmentation (R2-IOS) framework to address the challenging instance-level
object segmentation task. R2-IOS consists of a reversible proposal refinement
sub-network that predicts bounding box offsets for refining the object proposal
locations, and an instance-level segmentation sub-network that generates the
foreground mask of the dominant object instance in each proposal. By being
recursive, R2-IOS iteratively optimizes the two sub-networks during joint
training, in which the refined object proposals and improved segmentation
predictions are alternately fed into each other to progressively increase the
network capabilities. By being reversible, the proposal refinement sub-network
adaptively determines an optimal number of refinement iterations required for
each proposal during both training and testing. Furthermore, to handle multiple
overlapped instances within a proposal, an instance-aware denoising autoencoder
is introduced into the segmentation sub-network to distinguish the dominant
object from other distracting instances. Extensive experiments on the
challenging PASCAL VOC 2012 benchmark well demonstrate the superiority of
R2-IOS over other state-of-the-art methods. In particular, the
over classes at IoU achieves , which significantly
outperforms the results of by PFN~\cite{PFN} and
by~\cite{liu2015multi}.Comment: 9 page
Deep GrabCut for Object Selection
Most previous bounding-box-based segmentation methods assume the bounding box
tightly covers the object of interest. However it is common that a rectangle
input could be too large or too small. In this paper, we propose a novel
segmentation approach that uses a rectangle as a soft constraint by
transforming it into an Euclidean distance map. A convolutional encoder-decoder
network is trained end-to-end by concatenating images with these distance maps
as inputs and predicting the object masks as outputs. Our approach gets
accurate segmentation results given sloppy rectangles while being general for
both interactive segmentation and instance segmentation. We show our network
extends to curve-based input without retraining. We further apply our network
to instance-level semantic segmentation and resolve any overlap using a
conditional random field. Experiments on benchmark datasets demonstrate the
effectiveness of the proposed approaches.Comment: BMVC 201
Holistic, Instance-Level Human Parsing
Object parsing -- the task of decomposing an object into its semantic parts
-- has traditionally been formulated as a category-level segmentation problem.
Consequently, when there are multiple objects in an image, current methods
cannot count the number of objects in the scene, nor can they determine which
part belongs to which object. We address this problem by segmenting the parts
of objects at an instance-level, such that each pixel in the image is assigned
a part label, as well as the identity of the object it belongs to. Moreover, we
show how this approach benefits us in obtaining segmentations at coarser
granularities as well. Our proposed network is trained end-to-end given
detections, and begins with a category-level segmentation module. Thereafter, a
differentiable Conditional Random Field, defined over a variable number of
instances for every input image, reasons about the identity of each part by
associating it with a human detection. In contrast to other approaches, our
method can handle the varying number of people in each image and our holistic
network produces state-of-the-art results in instance-level part and human
segmentation, together with competitive results in category-level part
segmentation, all achieved by a single forward-pass through our neural network.Comment: Poster at BMVC 201
Adaptive Temporal Encoding Network for Video Instance-level Human Parsing
Beyond the existing single-person and multiple-person human parsing tasks in
static images, this paper makes the first attempt to investigate a more
realistic video instance-level human parsing that simultaneously segments out
each person instance and parses each instance into more fine-grained parts
(e.g., head, leg, dress). We introduce a novel Adaptive Temporal Encoding
Network (ATEN) that alternatively performs temporal encoding among key frames
and flow-guided feature propagation from other consecutive frames between two
key frames. Specifically, ATEN first incorporates a Parsing-RCNN to produce the
instance-level parsing result for each key frame, which integrates both the
global human parsing and instance-level human segmentation into a unified
model. To balance between accuracy and efficiency, the flow-guided feature
propagation is used to directly parse consecutive frames according to their
identified temporal consistency with key frames. On the other hand, ATEN
leverages the convolution gated recurrent units (convGRU) to exploit temporal
changes over a series of key frames, which are further used to facilitate the
frame-level instance-level parsing. By alternatively performing direct feature
propagation between consistent frames and temporal encoding network among key
frames, our ATEN achieves a good balance between frame-level accuracy and time
efficiency, which is a common crucial problem in video object segmentation
research. To demonstrate the superiority of our ATEN, extensive experiments are
conducted on the most popular video segmentation benchmark (DAVIS) and a newly
collected Video Instance-level Parsing (VIP) dataset, which is the first video
instance-level human parsing dataset comprised of 404 sequences and over 20k
frames with instance-level and pixel-wise annotations.Comment: To appear in ACM MM 2018. Code link:
https://github.com/HCPLab-SYSU/ATEN. Dataset link: http://sysu-hcp.net/li
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