Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discriminating Spatial and Temporal Relevance in Deep Taylor
  Decompositions for Explainable Activity Recognition   Current techniques for explainable AI have been applied with some success to
image processing. The recent rise of research in video processing has called
for similar work n deconstructing and explaining spatio-temporal models. While
many techniques are designed for 2D convolutional models, others are inherently
applicable to any input domain. One such body of work, deep Taylor
decomposition, propagates relevance from the model output distributively onto
its input and thus is not restricted to image processing models. However, by
exploiting a simple technique that removes motion information, we show that it
is not the case that this technique is effective as-is for representing
relevance in non-image tasks. We instead propose a discriminative method that
produces a na\"ive representation of both the spatial and temporal relevance of
a frame as two separate objects. This new discriminative relevance model
exposes relevance in the frame attributed to motion, that was previously
ambiguous in the original explanation. We observe the effectiveness of this
technique on a range of samples from the UCF-101 action recognition dataset,
two of which are demonstrated in this paper.

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The categories of this paper are: ao-sci
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: math
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The categories of this paper are: funct-an
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: q-alg
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: eess