23,957 research outputs found
Localization-Aware Active Learning for Object Detection
Active learning - a class of algorithms that iteratively searches for the
most informative samples to include in a training dataset - has been shown to
be effective at annotating data for image classification. However, the use of
active learning for object detection is still largely unexplored as determining
informativeness of an object-location hypothesis is more difficult. In this
paper, we address this issue and present two metrics for measuring the
informativeness of an object hypothesis, which allow us to leverage active
learning to reduce the amount of annotated data needed to achieve a target
object detection performance. Our first metric measures 'localization
tightness' of an object hypothesis, which is based on the overlapping ratio
between the region proposal and the final prediction. Our second metric
measures 'localization stability' of an object hypothesis, which is based on
the variation of predicted object locations when input images are corrupted by
noise. Our experimental results show that by augmenting a conventional
active-learning algorithm designed for classification with the proposed
metrics, the amount of labeled training data required can be reduced up to 25%.
Moreover, on PASCAL 2007 and 2012 datasets our localization-stability method
has an average relative improvement of 96.5% and 81.9% over the baseline method
using classification only
Visual object localization in image collections
Conference Name:6th International Conference on Image and Graphics, ICIG 2011. Conference Address: Hefei, Anhui, China. Time:August 12, 2011 - August 15, 2011.National Natural Science Foundation of China; Chinese Academy of Science; Microsoft Research Asia; Xian Institute of Optics and Precision Mechanics of CAS; Anhui Crearo Technology Co., LtdThe research of object localization is active in the field of visual object category. In this paper, we focus on object localization in a given special category dataset. We propose to exploit the context aware category discovery for object localization without any labeled examples. Firstly, the image is segmented based on a multiple segmentation algorithm. Secondly, these generated regions are clustered by spectral clustering method to find the category pattern based on the context of the dataset and the saliency. Thirdly, the object is localized based on the weakly supervised learning algorithm. To justify the effectiveness of the proposed method, the detection precision is employed to evaluate the performance of our approach. The experimental results demonstrate that our approach is promising in object localization with unsupervised learning method. ? 2011 IEEE
Deformable Part-based Fully Convolutional Network for Object Detection
Existing region-based object detectors are limited to regions with fixed box
geometry to represent objects, even if those are highly non-rectangular. In
this paper we introduce DP-FCN, a deep model for object detection which
explicitly adapts to shapes of objects with deformable parts. Without
additional annotations, it learns to focus on discriminative elements and to
align them, and simultaneously brings more invariance for classification and
geometric information to refine localization. DP-FCN is composed of three main
modules: a Fully Convolutional Network to efficiently maintain spatial
resolution, a deformable part-based RoI pooling layer to optimize positions of
parts and build invariance, and a deformation-aware localization module
explicitly exploiting displacements of parts to improve accuracy of bounding
box regression. We experimentally validate our model and show significant
gains. DP-FCN achieves state-of-the-art performances of 83.1% and 80.9% on
PASCAL VOC 2007 and 2012 with VOC data only.Comment: Accepted to BMVC 2017 (oral
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
The problem of computing category agnostic bounding box proposals is utilized
as a core component in many computer vision tasks and thus has lately attracted
a lot of attention. In this work we propose a new approach to tackle this
problem that is based on an active strategy for generating box proposals that
starts from a set of seed boxes, which are uniformly distributed on the image,
and then progressively moves its attention on the promising image areas where
it is more likely to discover well localized bounding box proposals. We call
our approach AttractioNet and a core component of it is a CNN-based category
agnostic object location refinement module that is capable of yielding accurate
and robust bounding box predictions regardless of the object category.
We extensively evaluate our AttractioNet approach on several image datasets
(i.e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on
all of them state-of-the-art results that surpass the previous work in the
field by a significant margin and also providing strong empirical evidence that
our approach is capable to generalize to unseen categories. Furthermore, we
evaluate our AttractioNet proposals in the context of the object detection task
using a VGG16-Net based detector and the achieved detection performance on COCO
manages to significantly surpass all other VGG16-Net based detectors while even
being competitive with a heavily tuned ResNet-101 based detector. Code as well
as box proposals computed for several datasets are available at::
https://github.com/gidariss/AttractioNet.Comment: Technical report. Code as well as box proposals computed for several
datasets are available at:: https://github.com/gidariss/AttractioNe
- …