1,433,839 research outputs found
3D Object Class Detection in the Wild
Object class detection has been a synonym for 2D bounding box localization
for the longest time, fueled by the success of powerful statistical learning
techniques, combined with robust image representations. Only recently, there
has been a growing interest in revisiting the promise of computer vision from
the early days: to precisely delineate the contents of a visual scene, object
by object, in 3D. In this paper, we draw from recent advances in object
detection and 2D-3D object lifting in order to design an object class detector
that is particularly tailored towards 3D object class detection. Our 3D object
class detection method consists of several stages gradually enriching the
object detection output with object viewpoint, keypoints and 3D shape
estimates. Following careful design, in each stage it constantly improves the
performance and achieves state-ofthe-art performance in simultaneous 2D
bounding box and viewpoint estimation on the challenging Pascal3D+ dataset
ClassCut for Unsupervised Class Segmentation
Abstract. We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].
Automatic annotation for weakly supervised learning of detectors
PhDObject detection in images and action detection in videos are among the most widely studied
computer vision problems, with applications in consumer photography, surveillance, and automatic
media tagging. Typically, these standard detectors are fully supervised, that is they require
a large body of training data where the locations of the objects/actions in images/videos have
been manually annotated. With the emergence of digital media, and the rise of high-speed internet,
raw images and video are available for little to no cost. However, the manual annotation
of object and action locations remains tedious, slow, and expensive. As a result there has been
a great interest in training detectors with weak supervision where only the presence or absence
of object/action in image/video is needed, not the location. This thesis presents approaches for
weakly supervised learning of object/action detectors with a focus on automatically annotating
object and action locations in images/videos using only binary weak labels indicating the presence
or absence of object/action in images/videos.
First, a framework for weakly supervised learning of object detectors in images is presented.
In the proposed approach, a variation of multiple instance learning (MIL) technique for automatically
annotating object locations in weakly labelled data is presented which, unlike existing
approaches, uses inter-class and intra-class cue fusion to obtain the initial annotation. The initial
annotation is then used to start an iterative process in which standard object detectors are used to
refine the location annotation. Finally, to ensure that the iterative training of detectors do not drift
from the object of interest, a scheme for detecting model drift is also presented. Furthermore,
unlike most other methods, our weakly supervised approach is evaluated on data without manual
pose (object orientation) annotation.
Second, an analysis of the initial annotation of objects, using inter-class and intra-class cues,
is carried out. From the analysis, a new method based on negative mining (NegMine) is presented
for the initial annotation of both object and action data. The NegMine based approach is a
much simpler formulation using only inter-class measure and requires no complex combinatorial
optimisation but can still meet or outperform existing approaches including the previously pre3
sented inter-intra class cue fusion approach. Furthermore, NegMine can be fused with existing
approaches to boost their performance.
Finally, the thesis will take a step back and look at the use of generic object detectors as prior
knowledge in weakly supervised learning of object detectors. These generic object detectors are
typically based on sampling saliency maps that indicate if a pixel belongs to the background
or foreground. A new approach to generating saliency maps is presented that, unlike existing
approaches, looks beyond the current image of interest and into images similar to the current
image. We show that our generic object proposal method can be used by itself to annotate the
weakly labelled object data with surprisingly high accuracy
Activity Driven Weakly Supervised Object Detection
Weakly supervised object detection aims at reducing the amount of supervision
required to train detection models. Such models are traditionally learned from
images/videos labelled only with the object class and not the object bounding
box. In our work, we try to leverage not only the object class labels but also
the action labels associated with the data. We show that the action depicted in
the image/video can provide strong cues about the location of the associated
object. We learn a spatial prior for the object dependent on the action (e.g.
"ball" is closer to "leg of the person" in "kicking ball"), and incorporate
this prior to simultaneously train a joint object detection and action
classification model. We conducted experiments on both video datasets and image
datasets to evaluate the performance of our weakly supervised object detection
model. Our approach outperformed the current state-of-the-art (SOTA) method by
more than 6% in mAP on the Charades video dataset.Comment: CVPR'19 camera read
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