562 research outputs found
Hypothesis-driven Online Video Stream Learning with Augmented Memory
The ability to continuously acquire new knowledge without forgetting previous
tasks remains a challenging problem for computer vision systems. Standard
continual learning benchmarks focus on learning from static iid images in an
offline setting. Here, we examine a more challenging and realistic online
continual learning problem called online stream learning. Like humans, some AI
agents have to learn incrementally from a continuous temporal stream of
non-repeating data. We propose a novel model, Hypotheses-driven Augmented
Memory Network (HAMN), which efficiently consolidates previous knowledge using
an augmented memory matrix of "hypotheses" and replays reconstructed image
features to avoid catastrophic forgetting. Compared with pixel-level and
generative replay approaches, the advantages of HAMN are two-fold. First,
hypothesis-based knowledge consolidation avoids redundant information in the
image pixel space and makes memory usage far more efficient. Second, hypotheses
in the augmented memory can be re-used for learning new tasks, improving
generalization and transfer learning ability. Given a lack of online
incremental class learning datasets on video streams, we introduce and adapt
two additional video datasets, Toybox and iLab, for online stream learning. We
also evaluate our method on the CORe50 and online CIFAR100 datasets. Our method
performs significantly better than all state-of-the-art methods, while offering
much more efficient memory usage. All source code and data are publicly
available at https://github.com/kreimanlab/AugMe
ARCHANGEL: Tamper-proofing Video Archives using Temporal Content Hashes on the Blockchain
We present ARCHANGEL; a novel distributed ledger based system for assuring
the long-term integrity of digital video archives. First, we describe a novel
deep network architecture for computing compact temporal content hashes (TCHs)
from audio-visual streams with durations of minutes or hours. Our TCHs are
sensitive to accidental or malicious content modification (tampering) but
invariant to the codec used to encode the video. This is necessary due to the
curatorial requirement for archives to format shift video over time to ensure
future accessibility. Second, we describe how the TCHs (and the models used to
derive them) are secured via a proof-of-authority blockchain distributed across
multiple independent archives. We report on the efficacy of ARCHANGEL within
the context of a trial deployment in which the national government archives of
the United Kingdom, Estonia and Norway participated.Comment: Accepted to CVPR Blockchain Workshop 201
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