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Multi-Grained Video Anomaly Detection — CVPR Poster

A CVPR 2024 poster presenting a multi-grained framework for video anomaly detection. It compares the proposed method against baselines like PMem and RoadMap, details three proxy tasks (continuity, discontinuity, missing frame), and shows SOTA results on Avenue and ShanghaiTech datasets.

Paper context

Paper title: Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning Abstract: A CVPR 2024 poster presenting a multi-grained framework for video anomaly detection. It compares the proposed method against baselines like PMem and RoadMap, details three proxy tasks (continuity, discontinuity, missing frame), and shows SOTA results on Avenue and ShanghaiTech datasets. Paper body (method & results): <!DOCTYPE html><html lang="en"> <head> <meta http-equiv="content-type" content="text/html; charset=UTF-8"> <title>CollideNet: Hierarchical Multi-scale Video Representation Learning with Disentanglement for Time-To-Collision Forecasting</title>  <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <link rel="stylesheet" href="/static/browse/0.3.4/css/arxiv-html-papers-20260131.css" type="text/css"> <script src="/static/browse/0.3.4/js/arxiv-html-papers-20260131.js"></script> <script> // Restore the saved color scheme preference, or // enact the browser preference if "automatic", // without expecting DOM load to have completed. // // Also restore any saved readingmode and ToC display preferences. function initializeReadingPreferences() { let saved_theme = localStorage.getItem("ar5iv_theme") || "automatic"; if (saved_theme === "automatic") { if (window.matchMedia("(prefers-color-scheme: dark)").matches) { saved_theme = "dark"; } } if (saved_theme == "dark") { document.documentElement.setAttribute("data-theme", "dark"); } else { document.documentElement.setAttribute("data-theme", "light"); } const tocDisplay = localStorage.getItem('arxiv_html_paper_toc_display'); if (tocDisplay) { document.documentElement.setAttribute("data-toc-display", tocDisplay); } const readingMode = localStorage.getItem('arxiv_html_paper_reading_mode'); if (readingMode) { document.documentElement.setAttribute("data-reading-mode", readingMode); } } // Run as soon as JS starts, to minimize repainting initializeReadingPreferences(); </script> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <link rel="stylesheet" type="text/css" media="screen" href="https://use.typekit.net/utz6mli.css"> </head> <body> <dialog id="modal-form" aria-labelledby="modal-title" closedby="any"> <form id="modal-form-content" method="dialog" enctype="multipart/form-data"> <header class="modal-header"> <h5 id="modal-title" class="modal-title">Report GitHub Issue</h5> <button type="submit" formnovalidate class="modal-close" aria-label="Close">×</button> </header> <div class="modal-body"> <label for="form_title">Title:</label> <input class="form-control" id="form_title" name="form_title" required placeholder="Enter title"> <p id="selectedTextModalDescription" hidden>Content selection saved. 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title="In CollideNet: Hierarchical Multi-scale Video Representation Learning with

The prompt

Above I've shared:
(1) the full paper text,
(2) all paper figures labeled by figure number,
(3) the caption for the central poster figure I'm building.

TASK: This is a CONFERENCE POSTER. **NOT** an academic-paper figure.
Style requirements:

  - Multi-section layout with a clear poster structure: large title banner
    at the top with the paper title + author/affiliation strip, then 3-6
    distinct content panels arranged in columns or a grid.
  - Large legible fonts (text must be readable at 2 m viewing distance) —
    headings ≥ 60 pt visual size in the final image.
  - Use colour blocks / panel backgrounds to delineate sections (this is
    what makes it a poster, not a single-figure diagram).
  - Aspect ratio: portrait or landscape rectangle, NOT square.

If your output looks like a standard academic-paper figure (single panel,
no title banner, dense small text, no colour blocks), you've failed the
task. Render the COMPLETE poster, not just the central figure.

Just give me the final poster image.

Try this prompt now

Open it inside the generator with the prompt pre-filled.

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Multi-Grained Video Anomaly Detection — CVPR Poster

Paper context

The prompt

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