Opening remarks: Towards Human-Like Visual Learning and 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)
- Track:
- Towards Human-Like Visual Learning & Reasoning
- Date:
- Speakers:
- Wenjun Zeng
- Affiliation:
- Microsoft Research
Towards Human-Like Visual Learning & Reasoning
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Opening remarks: Towards Human-Like Visual Learning and Reasoning
Speakers:- Wenjun Zeng
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Research talks: Learning for interpretability
Speakers:- Yuwang Wang,
- Hanwang Zhang,
- Shujian Yu
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Research talks: Few-shot and zero-shot visual learning and reasoning
Speakers:- Han Hu,
- Zhe Gan,
- Kyoung Mu Lee
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Panel: Computer vision in the next decade: Deeper or broader
Speakers:- Xilin Chen,
- Kyoung Mu Lee,
- Yi Ma
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Research talks: Generalization and adaptation
Speakers:- Suha Kwak,
- Chong Luo,
- Lu Yuan
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Closing remarks: Towards Human-Like Visual Learning and Reasoning
Speakers:- Yan Lu
