mlsanityData to 7 Oct 2026

3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding

arXiv 2212.06833 ↗First posted 13 Dec 2022cs.CV cs.AI cs.LGcited by 3

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Abstract (arXiv)

Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community. Recent research efforts have quickly led to the design of novel algorithms able to reduce the impact of the catastrophic forgetting phenomenon in deep neural networks. Due to this surge of interest in the field, many competitions have been held in recent years, as they are an excellent opportunity to stimulate research in promising directions. This paper summarizes the ideas, design choices, rules, and results of the challenge held at the 3rd Continual Learning in Computer Vision (CLVision) Workshop at CVPR 2022. The focus of this competition is the complex continual object detection task, which is still underexplored in literature compared to classification tasks. The challenge is based on the challenge version of the novel EgoObjects dataset, a large-scale egocentric object dataset explicitly designed to benchmark continual learning algorithms for egocentric category-/instance-level object understanding, which covers more than 1k unique main objects and 250+ categories in around 100k video frames.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 60 of its 82 references that are in the map. First posted on arXiv 13 Dec 2022.

Builds on

60 of 82 references.

References · 60

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38 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understandingcited by 39
  2. 2024Continual Learning in the Presence of Repetitioncited by 10
  3. 2025Continual Hyperbolic Learning of Instances and Classescited by 4