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        content="D-STEP: Dynamic Spatio-Temporal Pruning.">
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          <h1 class="title is-1 publication-title"><b>D-STEP: Dynamic Spatio-Temporal Pruning</b></h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://www.linkedin.com/in/avraham-raviv-47b3b5158/">Avraham Raviv</a>,</span>
            <span class="author-block">
              <a href="https://www.linkedin.com/in/yonatan-dinai-77295026/">Yonatan Dinai</a>,</span>
            <span class="author-block">
              <a href="https://www.linkedin.com/in/igor-drozdov-895581176/">Igor Drozdov</a>,</span>
            <span class="author-block">
              <a href="https://www.google.co.il/">Niv Zehngut</a>
            </span>
                and
            <span class="author-block">
              <a href="https://www.linkedin.com/in/ishay-goldin-91624117/">Ishay Goldin</a>
            </span>
          </div>

          <div class="is-size-5 publication-authors">
            <span class="author-block">Samsung Israel R&D Center</span>
          </div>

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        <h2 class="title is-3"><b>Abstract</b></h2>
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          <p>
            Video processing requires analysis of spatial features that are changing over time.
            By combining spatial and temporal modelling together,
            a neural network can gain a better understanding of the scene with no increase in computation.
          </p>
          <p>
          Spatio-temporal modeling
          can also be used to identify redundant and sparse information in both the spatial and

          the temporal domains. In this work we present <em><b>Dynamic Spatio-Temporal Pruning, D-
          STEP</b></em>, a new, simple, yet efficient method for learning the evolution of spatial mapping

          between frames. More specifically, we used a cascade of lightweight policy networks to
          dynamically filter out, per input, regions and channels that do not provide information
          while also sharing information across time. Guided by the policy networks, the model is
          able to focus on relevant data and filters, avoiding unnecessary computations.
          </p>
          <p>
          Extensive
            evaluations on Something-Something-V2, Jester and Mini-Kinetics action recognition

            datasets demonstrate that the proposed method shows a significantly improved accuracy-
            compute trade-off over the current state-of-the-art methods.
          </p>
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        <h3 class="title is-3"><i>Ablation Study</i></h4>
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         <h3 class="title is-3"><i>Qualitive Results – Spatial Sparsity</i></h4>
          <div class="publication-video">
          <img src="static/images/spatial_sparsity.png" alt="results2"/>
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  <div class="container is-max-desktop content">
    <h2 class="title">BibTeX</h2>
    <pre><code>@article{RDDZG2022,
  author    = {Raviv, Avraham and Dinai, Yonatan and Drozdov, Igor and Zenghut, Niv and Goldin, Ishay},
  title     = {D-STEP: Dynamic Spatio-Temporal Pruning},
  journal   = {BMVC},
  year      = {2022},
}</code></pre>
  </div>
</section>


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