670 research outputs found
Few-shot Object Detection on Remote Sensing Images
In this paper, we deal with the problem of object detection on remote sensing
images. Previous methods have developed numerous deep CNN-based methods for
object detection on remote sensing images and the report remarkable
achievements in detection performance and efficiency. However, current
CNN-based methods mostly require a large number of annotated samples to train
deep neural networks and tend to have limited generalization abilities for
unseen object categories. In this paper, we introduce a few-shot learning-based
method for object detection on remote sensing images where only a few annotated
samples are provided for the unseen object categories. More specifically, our
model contains three main components: a meta feature extractor that learns to
extract feature representations from input images, a reweighting module that
learn to adaptively assign different weights for each feature representation
from the support images, and a bounding box prediction module that carries out
object detection on the reweighted feature maps. We build our few-shot object
detection model upon YOLOv3 architecture and develop a multi-scale object
detection framework. Experiments on two benchmark datasets demonstrate that
with only a few annotated samples our model can still achieve a satisfying
detection performance on remote sensing images and the performance of our model
is significantly better than the well-established baseline models.Comment: 12pages, 7 figure
GenDet: Meta Learning to Generate Detectors From Few Shots
Object detection has made enormous progress and has been widely used in many applications. However, it performs poorly when only limited training data is available for novel classes that the model has never seen before. Most existing approaches solve few-shot detection tasks implicitly without directly modeling the detectors for novel classes. In this article, we propose GenDet, a new meta-learning-based framework that can effectively generate object detectors for novel classes from few shots and, thus, conducts few-shot detection tasks explicitly. The detector generator is trained by numerous few-shot detection tasks sampled from base classes each with sufficient samples, and thus, it is expected to generalize well on novel classes. An adaptive pooling module is further introduced to suppress distracting samples and aggregate the detectors generated from multiple shots. Moreover, we propose to train a reference detector for each base class in the conventional way, with which to guide the training of the detector generator. The reference detectors and the detector generator can be trained simultaneously. Finally, the generated detectors of different classes are encouraged to be orthogonal to each other for better generalization. The proposed approach is extensively evaluated on the ImageNet, VOC, and COCO data sets under various few-shot detection settings, and it achieves new state-of-the-art results
3D Object Detection Using Scale Invariant and Feature Reweighting Networks
3D object detection plays an important role in a large number of real-world
applications. It requires us to estimate the localizations and the orientations
of 3D objects in real scenes. In this paper, we present a new network
architecture which focuses on utilizing the front view images and frustum point
clouds to generate 3D detection results. On the one hand, a PointSIFT module is
utilized to improve the performance of 3D segmentation. It can capture the
information from different orientations in space and the robustness to
different scale shapes. On the other hand, our network obtains the useful
features and suppresses the features with less information by a SENet module.
This module reweights channel features and estimates the 3D bounding boxes more
effectively. Our method is evaluated on both KITTI dataset for outdoor scenes
and SUN-RGBD dataset for indoor scenes. The experimental results illustrate
that our method achieves better performance than the state-of-the-art methods
especially when point clouds are highly sparse.Comment: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19
Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild
Detecting objects and estimating their viewpoint in images are key tasks of
3D scene understanding. Recent approaches have achieved excellent results on
very large benchmarks for object detection and viewpoint estimation. However,
performances are still lagging behind for novel object categories with few
samples. In this paper, we tackle the problems of few-shot object detection and
few-shot viewpoint estimation. We propose a meta-learning framework that can be
applied to both tasks, possibly including 3D data. Our models improve the
results on objects of novel classes by leveraging on rich feature information
originating from base classes with many samples. A simple joint feature
embedding module is proposed to make the most of this feature sharing. Despite
its simplicity, our method outperforms state-of-the-art methods by a large
margin on a range of datasets, including PASCAL VOC and MS COCO for few-shot
object detection, and Pascal3D+ and ObjectNet3D for few-shot viewpoint
estimation. And for the first time, we tackle the combination of both few-shot
tasks, on Object- Net3D, showing promising results. Our code and data are
available at http://imagine.enpc.fr/~xiaoy/FSDetView/.Comment: Accepted as Poster at ECCV 2020, project website:
http://imagine.enpc.fr/~xiaoy/FSDetView
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