Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback

• URL: http://arxiv.org/abs/2304.04901v2
• Date: Wed, 06 Nov 2024 23:32:27 GMT
• Title: Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback
• Authors: Takuya Kiyokawa, Naoki Shirakura, Hiroki Katayama, Keita Tomochika, Jun Takamatsu,
• Abstract summary: Training deep-learning-based vision systems require the manual annotation of a significant number of images. We propose a human-in-the-loop dataset collection method that uses a web application. To counterbalance the workload and performance by encouraging the collection of multi-view object image datasets in an enjoyable manner, we propose three types of online visual feedback features.
• Score: 5.015678820698308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training deep-learning-based vision systems require the manual annotation of a significant number of images. Such manual annotation is highly time-consuming and labor-intensive. Although previous studies have attempted to eliminate the effort required for annotation, the effort required for image collection was retained. To address this, we propose a human-in-the-loop dataset collection method that uses a web application. To counterbalance the workload and performance by encouraging the collection of multi-view object image datasets in an enjoyable manner, thereby amplifying motivation, we propose three types of online visual feedback features to track the progress of the collection status. Our experiments thoroughly investigated the impact of each feature on collection performance and quality of operation. The results suggested the feasibility of annotation and object detection.

Related papers

• Feedback-driven object detection and iterative model improvement [2.3700911865675187]
  We present the development and evaluation of a platform designed to interactively improve object detection models. The platform allows uploading and annotating images as well as fine-tuning object detection models. We show evidence for a significant time reduction of up to 53% for semi-automatic compared to manual annotation.
  arXiv  Detail & Related papers  (2024-11-29T16:45:25Z)
• Harnessing Diffusion Models for Visual Perception with Meta Prompts [68.78938846041767]
  We propose a simple yet effective scheme to harness a diffusion model for visual perception tasks. We introduce learnable embeddings (meta prompts) to the pre-trained diffusion models to extract proper features for perception. Our approach achieves new performance records in depth estimation tasks on NYU depth V2 and KITTI, and in semantic segmentation task on CityScapes.
  arXiv  Detail & Related papers  (2023-12-22T14:40:55Z)
• Labeling Indoor Scenes with Fusion of Out-of-the-Box Perception Models [4.157013247909771]
  We propose to leverage the recent advancements in state-of-the-art models for bottom-up segmentation (SAM), object detection (Detic), and semantic segmentation (MaskFormer) We aim to develop a cost-effective labeling approach to obtain pseudo-labels for semantic segmentation and object instance detection in indoor environments. We demonstrate the effectiveness of the proposed approach on the Active Vision dataset and the ADE20K dataset.
  arXiv  Detail & Related papers  (2023-11-17T21:58:26Z)
• Accelerating exploration and representation learning with offline pre-training [52.6912479800592]
  We show that exploration and representation learning can be improved by separately learning two different models from a single offline dataset. We show that learning a state representation using noise-contrastive estimation and a model of auxiliary reward can significantly improve the sample efficiency on the challenging NetHack benchmark.
  arXiv  Detail & Related papers  (2023-03-31T18:03:30Z)
• Context-Matched Collage Generation for Underwater Invertebrate Detection [12.255951530970249]
  We introduce Context Matched Collages, which leverage explicit context labels to combine unused background examples with existing annotated data to synthesize additional training samples. By combining a set of our generated collage images with the original training set, we see improved performance using three different object detectors on DUSIA.
  arXiv  Detail & Related papers  (2022-11-15T20:08:16Z)
• CoDo: Contrastive Learning with Downstream Background Invariance for Detection [10.608660802917214]
  We propose a novel object-level self-supervised learning method, called Contrastive learning with Downstream background invariance (CoDo) The pretext task is converted to focus on instance location modeling for various backgrounds, especially for downstream datasets. Experiments on MSCOCO demonstrate that the proposed CoDo with common backbones, ResNet50-FPN, yields strong transfer learning results for object detection.
  arXiv  Detail & Related papers  (2022-05-10T01:26:15Z)
• Crop-Transform-Paste: Self-Supervised Learning for Visual Tracking [137.26381337333552]
  In this work, we develop the Crop-Transform-Paste operation, which is able to synthesize sufficient training data. Since the object state is known in all synthesized data, existing deep trackers can be trained in routine ways without human annotation.
  arXiv  Detail & Related papers  (2021-06-21T07:40:34Z)
• Few-Cost Salient Object Detection with Adversarial-Paced Learning [95.0220555274653]
  This paper proposes to learn the effective salient object detection model based on the manual annotation on a few training images only. We name this task as the few-cost salient object detection and propose an adversarial-paced learning (APL)-based framework to facilitate the few-cost learning scenario.
  arXiv  Detail & Related papers  (2021-04-05T14:15:49Z)
• 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)
• Embodied Visual Active Learning for Semantic Segmentation [33.02424587900808]
  We study the task of embodied visual active learning, where an agent is set to explore a 3d environment with the goal to acquire visual scene understanding. We develop a battery of agents - both learnt and pre-specified - and with different levels of knowledge of the environment. We extensively evaluate the proposed models using the Matterport3D simulator and show that a fully learnt method outperforms comparable pre-specified counterparts.
  arXiv  Detail & Related papers  (2020-12-17T11:02:34Z)
• From ImageNet to Image Classification: Contextualizing Progress on Benchmarks [99.19183528305598]
  We study how specific design choices in the ImageNet creation process impact the fidelity of the resulting dataset. Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for.
  arXiv  Detail & Related papers  (2020-05-22T17:39:16Z)

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.