Bounding Box Annotation with Visible Status
- URL: http://arxiv.org/abs/2304.04901v1
- Date: Tue, 11 Apr 2023 00:17:28 GMT
- Title: Bounding Box Annotation with Visible Status
- Authors: Takuya Kiyokawa, Naoki Shirakura, Hiroki Katayama, Keita Tomochika,
Jun Takamatsu
- Abstract summary: This study presents a mobile application-based free-viewpoint image-capturing method.
With the proposed application, users can collect multi-view image datasets automatically that are annotated with bounding boxes by moving the camera.
Our experiments demonstrated that using the gamified mobile application for bounding box annotation, with visible collection progress status, can motivate users to collect multi-view object image datasets.
- Score: 6.69350212746025
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Training deep-learning-based vision systems requires the manual annotation of
a significant amount of data to optimize several parameters of the deep
convolutional neural networks. Such manual annotation is highly time-consuming
and labor-intensive. To reduce this burden, a previous study presented a fully
automated annotation approach that does not require any manual intervention.
The proposed method associates a visual marker with an object and captures it
in the same image. However, because the previous method relied on moving the
object within the capturing range using a fixed-point camera, the collected
image dataset was limited in terms of capturing viewpoints. To overcome this
limitation, this study presents a mobile application-based free-viewpoint
image-capturing method. With the proposed application, users can collect
multi-view image datasets automatically that are annotated with bounding boxes
by moving the camera. However, capturing images through human involvement is
laborious and monotonous. Therefore, we propose gamified application features
to track the progress of the collection status. Our experiments demonstrated
that using the gamified mobile application for bounding box annotation, with
visible collection progress status, can motivate users to collect multi-view
object image datasets with less mental workload and time pressure in an
enjoyable manner, leading to increased engagement.
Related papers
- DiffUHaul: A Training-Free Method for Object Dragging in Images [78.93531472479202]
We propose a training-free method, dubbed DiffUHaul, for the object dragging task.
We first apply attention masking in each denoising step to make the generation more disentangled across different objects.
In the early denoising steps, we interpolate the attention features between source and target images to smoothly fuse new layouts with the original appearance.
arXiv Detail & Related papers (2024-06-03T17:59:53Z) - Learning to Select Camera Views: Efficient Multiview Understanding at
Few Glances [59.34619548026885]
We propose a view selection approach that analyzes the target object or scenario from given views and selects the next best view for processing.
Our approach features a reinforcement learning based camera selection module, MVSelect, that not only selects views but also facilitates joint training with the task network.
arXiv Detail & Related papers (2023-03-10T18:59:10Z) - Label Assistant: A Workflow for Assisted Data Annotation in Image
Segmentation Tasks [0.8135412538980286]
We propose a generic workflow to assist the annotation process and discuss methods on an abstract level.
Thereby, we review the possibilities of focusing on promising samples, image pre-processing, pre-labeling, label inspection, or post-processing of annotations.
In addition, we present an implementation of the proposal by means of a developed flexible and extendable software prototype nested in hybrid touchscreen/laptop device.
arXiv Detail & Related papers (2021-11-27T19:08:25Z) - Single Image Object Counting and Localizing using Active-Learning [4.56877715768796]
We present a new method for counting and localizing repeating objects in single-image scenarios.
Our method trains a CNN over a small set of labels carefully collected from the input image in few active-learning iterations.
Compared with existing user-assisted counting methods, our active-learning iterations achieve state-of-the-art performance in terms of counting and localizing accuracy, number of user mouse clicks, and running-time.
arXiv Detail & Related papers (2021-11-16T11:29:21Z) - Data Augmentation for Object Detection via Differentiable Neural
Rendering [71.00447761415388]
It is challenging to train a robust object detector when annotated data is scarce.
Existing approaches to tackle this problem include semi-supervised learning that interpolates labeled data from unlabeled data.
We introduce an offline data augmentation method for object detection, which semantically interpolates the training data with novel views.
arXiv Detail & Related papers (2021-03-04T06:31:06Z) - Instance Localization for Self-supervised Detection Pretraining [68.24102560821623]
We propose a new self-supervised pretext task, called instance localization.
We show that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning.
Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection.
arXiv Detail & Related papers (2021-02-16T17:58:57Z) - Ensembling object detectors for image and video data analysis [98.26061123111647]
We propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data.
We extend it to video data by proposing a two-stage tracking-based scheme for detection refinement.
arXiv Detail & Related papers (2021-02-09T12:38:16Z) - Self-supervised Human Detection and Segmentation via Multi-view
Consensus [116.92405645348185]
We propose a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training.
We show that our approach outperforms state-of-the-art self-supervised person detection and segmentation techniques on images that visually depart from those of standard benchmarks.
arXiv Detail & Related papers (2020-12-09T15:47:21Z) - Iterative Bounding Box Annotation for Object Detection [0.456877715768796]
We propose a semi-automatic method for efficient bounding box annotation.
The method trains the object detector iteratively on small batches of labeled images.
It learns to propose bounding boxes for the next batch, after which the human annotator only needs to correct possible errors.
arXiv Detail & Related papers (2020-07-02T08:40:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.