{
  "id": "2012.01201",
  "version": "v1",
  "published": "2020-12-02T13:36:51.000Z",
  "updated": "2020-12-02T13:36:51.000Z",
  "title": "Meta-Cognition-Based Simple And Effective Approach To Object Detection",
  "authors": [
    "Sannidhi P Kumar",
    "Chandan Gautam",
    "Suresh Sundaram"
  ],
  "categories": [
    "cs.CV",
    "cs.AI"
  ],
  "abstract": "Recently, many researchers have attempted to improve deep learning-based object detection models, both in terms of accuracy and operational speeds. However, frequently, there is a trade-off between speed and accuracy of such models, which encumbers their use in practical applications such as autonomous navigation. In this paper, we explore a meta-cognitive learning strategy for object detection to improve generalization ability while at the same time maintaining detection speed. The meta-cognitive method selectively samples the object instances in the training dataset to reduce overfitting. We use YOLO v3 Tiny as a base model for the work and evaluate the performance using the MS COCO dataset. The experimental results indicate an improvement in absolute precision of 2.6% (minimum), and 4.4% (maximum), with no overhead to inference time.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-12-02T13:36:51.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "meta-cognition-based simple",
      "effective approach",
      "deep learning-based object detection models",
      "ms coco dataset",
      "yolo v3 tiny"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}