[ContinualAI Seminars]: "vCLIMB: A Novel Video Class Incremental Learning Benchmark"
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Keiland Cooper
May 4, 2022, 4:12:42 AM5/4/22
to Continual Learning & AI News
Hi All,
This Thursday 05-05-2022, for the ContinualAI Seminar, Andrés Villa & Kumail Alhamoud (KAUST & Pontificia Universidad Católica de Chile) will present the paper:
Title: “vCLIMB: A Novel Video Class Incremental Learning Benchmark”
Abstract: Continual learning (CL) is under-explored in the video domain. The few existing works contain splits with imbalanced class distributions over the tasks, or study the problem in unsuitable datasets. We introduce vCLIMB, a novel video continual learning benchmark. vCLIMB is a standardized test-bed to analyze catastrophic forgetting of deep models in video continual learning. In contrast to previous work, we focus on class incremental continual learning with models trained on a sequence of disjoint tasks, and distribute the number of classes uniformly across the tasks. We perform in-depth evaluations of existing CL methods in vCLIMB, and observe two unique challenges in video data. The selection of instances to store in episodic memory is performed at the frame level. Second, untrimmed training data influences the effectiveness of frame sampling strategies. We address these two challenges by proposing a temporal consistency regularization that can be applied on top of memory-based continual learning methods. Our approach significantly improves the baseline, by up to 24% on the untrimmed continual learning task.
This Thursday 05-05-2022, for the ContinualAI Seminar, Andrés Villa & Kumail Alhamoud (KAUST & Pontificia Universidad Católica de Chile) will present the paper:
Title: “vCLIMB: A Novel Video Class Incremental Learning Benchmark”
Abstract: Continual learning (CL) is under-explored in the video domain. The few existing works contain splits with imbalanced class distributions over the tasks, or study the problem in unsuitable datasets. We introduce vCLIMB, a novel video continual learning benchmark. vCLIMB is a standardized test-bed to analyze catastrophic forgetting of deep models in video continual learning. In contrast to previous work, we focus on class incremental continual learning with models trained on a sequence of disjoint tasks, and distribute the number of classes uniformly across the tasks. We perform in-depth evaluations of existing CL methods in vCLIMB, and observe two unique challenges in video data. The selection of instances to store in episodic memory is performed at the frame level. Second, untrimmed training data influences the effectiveness of frame sampling strategies. We address these two challenges by proposing a temporal consistency regularization that can be applied on top of memory-based continual learning methods. Our approach significantly improves the baseline, by up to 24% on the untrimmed continual learning task.
See you there at:
UTC - 15:30 PM
CEST - 17:30 PM
PST - 8:30 AM
Don't miss a ContinualAI event! Subscribe to our new event calendar here!
---------------
- YouTube link: [click here]
- Microsoft Teams: [ click here ]
UTC - 15:30 PM
CEST - 17:30 PM
PST - 8:30 AM
Don't miss a ContinualAI event! Subscribe to our new event calendar here!
---------------
- YouTube link: [click here]
- Microsoft Teams: [ click here ]
- YouTube recordings of the previous sessions: https://www.youtube.com/c/ContinualAI
---------------
Feel free to share this email to anyone interested and invite them to subscribe this mailing-list here: https://groups.google.com/g/continualai
Please also contact me if you want to speak at one of the next sessions!
Looking forward to seeing you all there!
All the best,
Keiland Cooper
University of California
ContinualAI Co-founder
http://kwcooper.xyz
---------------
Feel free to share this email to anyone interested and invite them to subscribe this mailing-list here: https://groups.google.com/g/continualai
Please also contact me if you want to speak at one of the next sessions!
Looking forward to seeing you all there!
All the best,
Keiland Cooper
University of California
ContinualAI Co-founder
http://kwcooper.xyz
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