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Exploring the Challenges Towards Lifelong Fact Learning

  • Mohamed ElhoseinyEmail author
  • Francesca Babiloni
  • Rahaf Aljundi
  • Marcus Rohrbach
  • Manohar Paluri
  • Tinne Tuytelaars
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11366)

Abstract

So far life-long learning (LLL) has been studied in relatively small-scale and relatively artificial setups. Here, we introduce a new large-scale alternative. What makes the proposed setup more natural and closer to human-like visual systems is threefold: First, we focus on concepts (or facts, as we call them) of varying complexity, ranging from single objects to more complex structures such as objects performing actions, and objects interacting with other objects. Second, as in real-world settings, our setup has a long-tail distribution, an aspect which has mostly been ignored in the LLL context. Third, facts across tasks may share structure (e.g., \(\langle \)person, riding, wave\(\rangle \) and \(\langle \)dog, riding, wave\(\rangle \)). Facts can also be semantically related (e.g., “liger” relates to seen categories like “tiger” and “lion”). Given the large number of possible facts, a LLL setup seems a natural choice. To avoid model size growing over time and to optimally exploit the semantic relations and structure, we combine it with a visual semantic embedding instead of discrete class labels. We adapt existing datasets with the properties mentioned above into new benchmarks, by dividing them semantically or randomly into disjoint tasks. This leads to two large-scale benchmarks with 906,232 images and 165,150 unique facts, on which we evaluate and analyze state-of-the-art LLL methods.

Notes

Acknowledgements

Rahaf Aljundi’s research was funded by an FWO scholarship.

Supplementary material

484523_1_En_5_MOESM1_ESM.zip (19.6 mb)
Supplementary material 1 (zip 20019 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohamed Elhoseiny
    • 1
    Email author
  • Francesca Babiloni
    • 2
  • Rahaf Aljundi
    • 2
  • Marcus Rohrbach
    • 1
  • Manohar Paluri
    • 1
  • Tinne Tuytelaars
    • 2
  1. 1.Facebook AI ResearchPalo AltoUSA
  2. 2.KU LeuvenLeuvenBelgium

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