mlsanityData to 7 Oct 2026

Dynamic Object Comprehension: A Framework For Evaluating Artificial Visual Perception

arXiv 2202.08490 ↗First posted 17 Feb 2022cs.CV cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Augmented and Mixed Reality are emerging as likely successors to the mobile internet. However, many technical challenges remain. One of the key requirements of these systems is the ability to create a continuity between physical and virtual worlds, with the user's visual perception as the primary interface medium. Building this continuity requires the system to develop a visual understanding of the physical world. While there has been significant recent progress in computer vision and AI techniques such as image classification and object detection, success in these areas has not yet led to the visual perception required for these critical MR and AR applications. A significant issue is that current evaluation criteria are insufficient for these applications. To motivate and evaluate progress in this emerging area, there is a need for new metrics. In this paper we outline limitations of current evaluation criteria and propose new criteria.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 31 references that are in the map. First posted on arXiv 17 Feb 2022.

Builds on

9 of 31 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2014Going Deeper with Convolutionscited by 4,231
  4. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  5. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  6. 2018Object Detection with Deep Learning: A Reviewcited by 524
  7. 2014Multiple Object Tracking: A Literature Reviewcited by 165
  8. 2018Complex-YOLO: Real-time 3D Object Detection on Point Cloudscited by 56
  9. 2018RTSeg: Real-time Semantic Segmentation Comparative Studycited by 11

Built on by

Not cited yet.