31,415 research outputs found
Pseudo Mask Augmented Object Detection
In this work, we present a novel and effective framework to facilitate object
detection with the instance-level segmentation information that is only
supervised by bounding box annotation. Starting from the joint object detection
and instance segmentation network, we propose to recursively estimate the
pseudo ground-truth object masks from the instance-level object segmentation
network training, and then enhance the detection network with top-down
segmentation feedbacks. The pseudo ground truth mask and network parameters are
optimized alternatively to mutually benefit each other. To obtain the promising
pseudo masks in each iteration, we embed a graphical inference that
incorporates the low-level image appearance consistency and the bounding box
annotations to refine the segmentation masks predicted by the segmentation
network. Our approach progressively improves the object detection performance
by incorporating the detailed pixel-wise information learned from the
weakly-supervised segmentation network. Extensive evaluation on the detection
task in PASCAL VOC 2007 and 2012 [12] verifies that the proposed approach is
effective
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
3D Bounding Box Estimation Using Deep Learning and Geometry
We present a method for 3D object detection and pose estimation from a single
image. In contrast to current techniques that only regress the 3D orientation
of an object, our method first regresses relatively stable 3D object properties
using a deep convolutional neural network and then combines these estimates
with geometric constraints provided by a 2D object bounding box to produce a
complete 3D bounding box. The first network output estimates the 3D object
orientation using a novel hybrid discrete-continuous loss, which significantly
outperforms the L2 loss. The second output regresses the 3D object dimensions,
which have relatively little variance compared to alternatives and can often be
predicted for many object types. These estimates, combined with the geometric
constraints on translation imposed by the 2D bounding box, enable us to recover
a stable and accurate 3D object pose. We evaluate our method on the challenging
KITTI object detection benchmark both on the official metric of 3D orientation
estimation and also on the accuracy of the obtained 3D bounding boxes. Although
conceptually simple, our method outperforms more complex and computationally
expensive approaches that leverage semantic segmentation, instance level
segmentation and flat ground priors and sub-category detection. Our
discrete-continuous loss also produces state of the art results for 3D
viewpoint estimation on the Pascal 3D+ dataset.Comment: To appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Deep Interactive Region Segmentation and Captioning
With recent innovations in dense image captioning, it is now possible to
describe every object of the scene with a caption while objects are determined
by bounding boxes. However, interpretation of such an output is not trivial due
to the existence of many overlapping bounding boxes. Furthermore, in current
captioning frameworks, the user is not able to involve personal preferences to
exclude out of interest areas. In this paper, we propose a novel hybrid deep
learning architecture for interactive region segmentation and captioning where
the user is able to specify an arbitrary region of the image that should be
processed. To this end, a dedicated Fully Convolutional Network (FCN) named
Lyncean FCN (LFCN) is trained using our special training data to isolate the
User Intention Region (UIR) as the output of an efficient segmentation. In
parallel, a dense image captioning model is utilized to provide a wide variety
of captions for that region. Then, the UIR will be explained with the caption
of the best match bounding box. To the best of our knowledge, this is the first
work that provides such a comprehensive output. Our experiments show the
superiority of the proposed approach over state-of-the-art interactive
segmentation methods on several well-known datasets. In addition, replacement
of the bounding boxes with the result of the interactive segmentation leads to
a better understanding of the dense image captioning output as well as accuracy
enhancement for the object detection in terms of Intersection over Union (IoU).Comment: 17, pages, 9 figure
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