Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection

• URL: http://arxiv.org/abs/2312.13822v1
• Date: Thu, 21 Dec 2023 13:12:37 GMT
• Title: Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection
• Authors: Kwangrok Ryoo, Yeonsik Jo, Seungjun Lee, Mira Kim, Ahra Jo, Seung Hwan Kim, Seungryong Kim, Soonyoung Lee
• Abstract summary: We propose Universal-Noise. (UNA), a more practical setting that encompasses all types of noise that can occur in object detection. We analyzed the development direction of previous works of detection algorithms and examined the factors that impact the robustness of detection model learning method. We open-source the code for injecting UNA into the dataset and all the training log and weight are also shared.
• Score: 36.318411642128446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes. However, previous studies have only addressed certain types of noise (e.g., localization or categorization). In this paper, we propose Universal-Noise Annotation (UNA), a more practical setting that encompasses all types of noise that can occur in object detection, and analyze how UNA affects the performance of the detector. We analyzed the development direction of previous works of detection algorithms and examined the factors that impact the robustness of detection model learning method. We open-source the code for injecting UNA into the dataset and all the training log and weight are also shared.

Related papers

• Bayesian Detector Combination for Object Detection with Crowdsourced Annotations [49.43709660948812]
  Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise. We propose a novel Bayesian Detector Combination (BDC) framework to more effectively train object detectors with noisy crowdsourced annotations. BDC is model-agnostic, requires no prior knowledge of the annotators' skill level, and seamlessly integrates with existing object detection models.
  arXiv  Detail & Related papers  (2024-07-10T18:00:54Z)
• Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought [0.0]
  We study how noise in chain of thought impacts task performance in highly-controlled setting. We define two types of noise: textitstatic noise, a local form of noise which is applied after the CoT trace is computed, and textitdynamic noise, a global form of noise which propagates errors in the trace as it is computed. We find fine-tuned models are extremely robust to high levels of static noise but struggle significantly more with lower levels of dynamic noise.
  arXiv  Detail & Related papers  (2024-02-06T13:59:56Z)
• Robust Tiny Object Detection in Aerial Images amidst Label Noise [50.257696872021164]
  This study addresses the issue of tiny object detection under noisy label supervision. We propose a DeNoising Tiny Object Detector (DN-TOD), which incorporates a Class-aware Label Correction scheme. Our method can be seamlessly integrated into both one-stage and two-stage object detection pipelines.
  arXiv  Detail & Related papers  (2024-01-16T02:14:33Z)
• Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective [38.24239397999152]
  We propose a novel sample selection-based approach for noisy label learning, called Proto-semi. Proto-semi divides all samples into the confident and unconfident datasets via warm-up. By leveraging the confident dataset, prototype vectors are constructed to capture class characteristics. Empirical evaluations on a real-world annotated dataset substantiate the robustness of Proto-semi in handling the problem of learning from noisy labels.
  arXiv  Detail & Related papers  (2023-07-28T10:57:38Z)
• Improving the Robustness of Summarization Models by Detecting and Removing Input Noise [50.27105057899601]
  We present a large empirical study quantifying the sometimes severe loss in performance from different types of input noise for a range of datasets and model sizes. We propose a light-weight method for detecting and removing such noise in the input during model inference without requiring any training, auxiliary models, or even prior knowledge of the type of noise.
  arXiv  Detail & Related papers  (2022-12-20T00:33:11Z)
• Noisy Annotation Refinement for Object Detection [47.066070566714984]
  We propose a new problem setting of training object detectors on datasets with entangled noises of annotations of class labels and bounding boxes. Our proposed method efficiently decouples the entangled noises, corrects the noisy annotations, and subsequently trains the detector using the corrected annotations.
  arXiv  Detail & Related papers  (2021-10-20T09:39:50Z)
• Towards Noise-resistant Object Detection with Noisy Annotations [119.63458519946691]
  Training deep object detectors requires significant amount of human-annotated images with accurate object labels and bounding box coordinates. Noisy annotations are much more easily accessible, but they could be detrimental for learning. We address the challenging problem of training object detectors with noisy annotations, where the noise contains a mixture of label noise and bounding box noise.
  arXiv  Detail & Related papers  (2020-03-03T01:32:16Z)

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.