43 research outputs found
Scalable Object Detection using Deep Neural Networks
Deep convolutional neural networks have recently achieved state-of-the-art
performance on a number of image recognition benchmarks, including the ImageNet
Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on
the localization sub-task was a network that predicts a single bounding box and
a confidence score for each object category in the image. Such a model captures
the whole-image context around the objects but cannot handle multiple instances
of the same object in the image without naively replicating the number of
outputs for each instance. In this work, we propose a saliency-inspired neural
network model for detection, which predicts a set of class-agnostic bounding
boxes along with a single score for each box, corresponding to its likelihood
of containing any object of interest. The model naturally handles a variable
number of instances for each class and allows for cross-class generalization at
the highest levels of the network. We are able to obtain competitive
recognition performance on VOC2007 and ILSVRC2012, while using only the top few
predicted locations in each image and a small number of neural network
evaluations
Self-taught Object Localization with Deep Networks
This paper introduces self-taught object localization, a novel approach that
leverages deep convolutional networks trained for whole-image recognition to
localize objects in images without additional human supervision, i.e., without
using any ground-truth bounding boxes for training. The key idea is to analyze
the change in the recognition scores when artificially masking out different
regions of the image. The masking out of a region that includes the object
typically causes a significant drop in recognition score. This idea is embedded
into an agglomerative clustering technique that generates self-taught
localization hypotheses. Our object localization scheme outperforms existing
proposal methods in both precision and recall for small number of subwindow
proposals (e.g., on ILSVRC-2012 it produces a relative gain of 23.4% over the
state-of-the-art for top-1 hypothesis). Furthermore, our experiments show that
the annotations automatically-generated by our method can be used to train
object detectors yielding recognition results remarkably close to those
obtained by training on manually-annotated bounding boxes.Comment: WACV 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