3,628 research outputs found
RON: Reverse Connection with Objectness Prior Networks for Object Detection
We present RON, an efficient and effective framework for generic object
detection. Our motivation is to smartly associate the best of the region-based
(e.g., Faster R-CNN) and region-free (e.g., SSD) methodologies. Under fully
convolutional architecture, RON mainly focuses on two fundamental problems: (a)
multi-scale object localization and (b) negative sample mining. To address (a),
we design the reverse connection, which enables the network to detect objects
on multi-levels of CNNs. To deal with (b), we propose the objectness prior to
significantly reduce the searching space of objects. We optimize the reverse
connection, objectness prior and object detector jointly by a multi-task loss
function, thus RON can directly predict final detection results from all
locations of various feature maps. Extensive experiments on the challenging
PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO benchmarks demonstrate the
competitive performance of RON. Specifically, with VGG-16 and low resolution
384X384 input size, the network gets 81.3% mAP on PASCAL VOC 2007, 80.7% mAP on
PASCAL VOC 2012 datasets. Its superiority increases when datasets become larger
and more difficult, as demonstrated by the results on the MS COCO dataset. With
1.5G GPU memory at test phase, the speed of the network is 15 FPS, 3X faster
than the Faster R-CNN counterpart.Comment: Project page will be available at https://github.com/taokong/RON, and
formal paper will appear in CVPR 201
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation
Visual tracking is a fundamental problem in computer vision. Recently, some
deep-learning-based tracking algorithms have been achieving record-breaking
performances. However, due to the high complexity of deep learning, most deep
trackers suffer from low tracking speed, and thus are impractical in many
real-world applications. Some new deep trackers with smaller network structure
achieve high efficiency while at the cost of significant decrease on precision.
In this paper, we propose to transfer the feature for image classification to
the visual tracking domain via convolutional channel reductions. The channel
reduction could be simply viewed as an additional convolutional layer with the
specific task. It not only extracts useful information for object tracking but
also significantly increases the tracking speed. To better accommodate the
useful feature of the target in different scales, the adaptation filters are
designed with different sizes. The yielded visual tracker is real-time and also
illustrates the state-of-the-art accuracies in the experiment involving two
well-adopted benchmarks with more than 100 test videos.Comment: 6 page
A Review of Object Detection Models based on Convolutional Neural Network
Convolutional Neural Network (CNN) has become the state-of-the-art for object
detection in image task. In this chapter, we have explained different
state-of-the-art CNN based object detection models. We have made this review
with categorization those detection models according to two different
approaches: two-stage approach and one-stage approach. Through this chapter, it
has shown advancements in object detection models from R-CNN to latest
RefineDet. It has also discussed the model description and training details of
each model. Here, we have also drawn a comparison among those models.Comment: 17 pages, 11 figures, 1 tabl
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