Text to FigureText → Imageposter

MASA: Label-Free Any-Object Tracking — Poster

A conference poster presenting MASA, a method for generalizing Multiple Object Tracking (MOT) to any objects without tracking labels. It features model architecture diagrams, training and inference pipelines, quantitative results on benchmarks like TAO and BDD, and qualitative comparisons including t-SNE visualizations.

Paper context

Paper title: Matching Anything by Segmenting Anything (MASA) - CVPR 2024 Poster Abstract: A conference poster presenting MASA, a method for generalizing Multiple Object Tracking (MOT) to any objects without tracking labels. It features model architecture diagrams, training and inference pipelines, quantitative results on benchmarks like TAO and BDD, and qualitative comparisons including t-SNE visualizations. Paper body (method & results): <!DOCTYPE html> <html lang="en"> <head> <meta content="text/html; charset=utf-8" http-equiv="content-type"/> <title>Matching Anything by Segmenting Anything</title>  <meta content="width=device-width, initial-scale=1, shrink-to-fit=no" name="viewport"/> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet" type="text/css"/> <link href="/static/browse/0.3.4/css/ar5iv.0.7.9.min.css" rel="stylesheet" type="text/css"/> <link href="/static/browse/0.3.4/css/ar5iv-fonts.0.7.9.min.css" rel="stylesheet" type="text/css"/> <link href="/static/browse/0.3.4/css/latexml_styles.css" rel="stylesheet" type="text/css"/> <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/html2canvas/1.3.3/html2canvas.min.js"></script> <script src="/static/browse/0.3.4/js/addons_new.js"></script> <script src="/static/browse/0.3.4/js/feedbackOverlay.js"></script> <base href="/html/2406.04221v1/"/></head> <body> <nav class="ltx_page_navbar"> <nav class="ltx_TOC"> <ol class="ltx_toclist"> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S1" title="In Matching Anything by Segmenting Anything">1 Introduction</a></li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S2" title="In Matching Anything by Segmenting Anything">2 Related Work</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S2.SS1" title="In 2 Related Work ‣ Matching Anything by Segmenting Anything">2.1 Learning Instance-level Association</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S2.SS2" title="In 2 Related Work ‣ Matching Anything by Segmenting Anything">2.2 Segment and Track Anything Models</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S3" title="In Matching Anything by Segmenting Anything">3 Method</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S3.SS1" title="In 3 Method ‣ Matching Anything by Segmenting Anything">3.1 Preliminaries: SAM</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"> <a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S3.SS2" title="In 3 Method ‣ Matching Anything by Segmenting Anything">3.2 Matching Anything by Segmenting Anything</a> <ol class="ltx_toclist ltx_toclist_subsection"> <li class="ltx_tocentry ltx_tocentry_subsubsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S3.SS2.SSS1" title="In 3.2 Matching Anything by Segmenting Anything ‣ 3 Method ‣ Matching Anything by Segmenting Anything">3.2.1 MASA Pipeline</a></li> <li class="ltx_tocentry ltx_tocentry_subsubsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S3.SS2.SSS2" title="In 3.2 Matching Anything by Segmenting Anything ‣ 3 Method ‣ Matching Anything by Segmenting Anything">3.2.2 MASA Adapter</a></li> <li class="ltx_tocentry ltx_tocentry_subsubsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S3.SS2.SSS3" title="In 3.2 Matching Anything by Segmenting Anything ‣ 3 Method ‣ Matching Anything by Segmenting Anything">3.2.3 Inference</a></li> </ol> </li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S4" title="In Matching Anything by Segmenting Anything">4 Experiments</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S4.SS1" title="In 4 Experiments ‣ Matching Anything by Segmenting Anything">4.1 Experimental Setup</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S4.SS2" title="In 4 Experiments ‣ Matching Anything by Segmenting Anything">4.2 State-of-the-Art Comparison</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S4.SS3" title="In 4 Experiments ‣ Matching Anything by Segmenting Anything">4.3 Ablation Study and Analysis</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S5" title="In Matching Anything by Segmenting Anything">5 Conclusion</a></li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S6" title="In Matching Anything by Segmenting Anything">6 Acknowledgments</a></li> <li class="ltx_tocentry ltx_tocentry_part"> <a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#Pt1" title="In Matching Anything by Segmenting Anything">Appendix</a> <ol class="ltx_toclist ltx_toclist_part"> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S1a" title="In Appendix ‣ Matching Anything by Segmenting Anything">A Effectiveness on Other Backbones</a></li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S2a" title="In Appendix ‣ Matching Anything by Segmenting Anything">B Zero-shot Evaluation on YoutubeVIS</a></li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S3a" title="In Appendix ‣ Matching Anything by Segmenting Anything">C Visualization of Instance Embeddings</a></li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S4a" title="In Appendix ‣ Matching Anything by Segmenting Anything">D Domain Gap and Adaptation</a></li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S5a" title="In Appendix ‣ Matching Anything by Segmenting Anything">E Impact of Photometric Augmentation</a></li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S6a" title="In Appendix ‣ Matching Anything by Segmenting Anything">F Comparison of Proposal Diversity</a></li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S7" title="In Appendix ‣ Matching Anything by Segmenting Anything">G Compare with Self-Supervised Methods</a></li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S8" title="In Appendix ‣ Matching Anything by Segmenting Anything">H Comparison with VOS-based Methods</a></li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S9" title="In Appendix ‣ Matching Anything by Segmenting Anything">I More Qualitative Results</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S9.SS1" title="In I More Qualitative Results ‣ Appendix ‣ Matching Anything by Segmenting Anything">I.1 Fast Proposal Generation</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S9.SS2" title="In I More Qualitative Results ‣ Appendix ‣ Matching Anything by Segmenting Anything">I.2 Open-Vocabulary Tracking</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S9.SS3" title="In I More Qualitative Results ‣ Appendix ‣ Matching Anything by Segmenting Anything">I.3 Joint Segment and Track Everything</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S10" title="In Appendix ‣ Matching Anything by Segmenting Anything">J Implementation Details</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2406.04221v1#S10.SS1" title="In J Implementation Details ‣ Appendix ‣ Matching Anything by Segmenting Anything">J.1 Architecture Detail</a></l

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.

Try this prompt

Related prompts

Text to Figure

MASA: Label-Free Any-Object Tracking — Poster

Paper context

The prompt

Try this prompt now

Related prompts

PINN Framework Pipeline for Microscopy Data

DP-FTRL Privacy Amplification Analysis — Conference Poster

NC-TTT: Noise-Contrastive Test-Time Training Poster