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arXiv Computer Vision By Fabien Poirier, Myriam Lamolle

Adapting Visualization Techniques for Time-Series Anomaly Detection: From Convolutional Neural Networks to Convolutional-Recurrent Neural Networks

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The paper investigates how to adapt visualization techniques—such as saliency maps and Grad‑CAM—to convolutional‑recurrent neural networks (CNN+RNN) used for time‑series anomaly detection in video data. It combines VGG19 for feature extraction with a GRU for sequential analysis, noting that the TimeDistributed layer complicates gradient propagation and weakens the link between spatial and temporal information, thereby reducing the effectiveness of standard visualization methods. The authors propose adaptations of these techniques to better interpret models that incorporate a temporal dimension, highlighting both the challenges and the potential of extending static‑image interpretation strategies to temporal models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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