Closing remarks: Towards Human-Like Visual Learning and Reasoning
- Yan Lu | Microsoft Research Asia
- Microsoft Research Summit 2021 | Towards Human-Like Visual Learning & Reasoning
Big data-driven deep learning has helped significantly improve the performance of visual tasks in the past few years, but it has also exhibited limitations in scalability and adaptation to real-world scenarios. Researchers and practitioners are working hard to develop architectures and algorithms to address these limitations. In this track, researchers and practitioners share their work and insights and discuss how to effectively move this emerging field forward.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
Towards Human-Like Visual Learning & Reasoning
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Research talks: Learning for interpretability
- Hanwang Zhang,
- Yuwang Wang,
- Shujian Yu
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Research talks: Few-shot and zero-shot visual learning and reasoning
- Kyoung Mu Lee,
- Han Hu,
- Zhe Gan
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Panel: Computer vision in the next decade: Deeper or broader
- Xilin Chen,
- Kyoung Mu Lee,
- Yi Ma
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Research talks: Generalization and adaptation
- Suha Kwak,
- Chong Luo,
- Lu Yuan
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