{"id":13,"date":"2026-05-06T03:09:36","date_gmt":"2026-05-06T03:09:36","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf7\/?page_id=13"},"modified":"2026-05-06T15:02:40","modified_gmt":"2026-05-06T15:02:40","slug":"experiments","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf7\/experiments\/","title":{"rendered":"Experiments"},"content":{"rendered":"\n
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<\/div>\n<\/div>\n\n\n\n
\"\"<\/figure>
\n

Baseline<\/strong><\/h2>\n\n\n\n

Removing the dropout from slot attention head improves performance<\/p>\n\n\n\n

Baseline fails at identifying smaller objects and have blur boundaries.<\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

\n
\n

Remove Dropout<\/strong><\/h2>\n\n\n\n

Removing the dropout from slot attention head improves performance<\/p>\n<\/div>

\"\"<\/figure><\/div>\n<\/div>\n\n\n\n
\n

Background\/Foreground Aware<\/strong> Strategy<\/strong><\/h2>\n\n\n\n

Identifying the background first helps to separate objects from the background.<\/p>\n\n\n\n

\"\"<\/figure>\n<\/div>\n\n\n\n
\n

Geometric Consistency Loss<\/strong><\/h2>\n\n\n\n

Encourages slots to align with consistent 3D structure across frames<\/p>\n\n\n\n

Leads to spatially coherent activations, but introduces training instability.<\/p>\n\n\n\n

\"\"<\/figure>\n<\/div>\n\n\n\n
\n
\"\"<\/figure>
\n

Deeper Slot Head<\/strong><\/h2>\n\n\n\n
    \n
  • Performance degrades as the number of objects increases, but adopting a deeper head improves robustness in multi-object scenarios.<\/li>\n\n\n\n
  • The decoder with a deeper head learns meaningful semantic cues, as evidenced by improved performance on the KITTI.<\/li>\n<\/ul>\n<\/div><\/div>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"excerpt":{"rendered":"

    Baseline Removing the dropout from slot attention head improves performance Baseline fails at identifying smaller objects and have blur boundaries. Remove Dropout Removing the dropout from slot attention head improves performance Background\/Foreground Aware Strategy Identifying the background first helps to separate objects from the background. Geometric Consistency Loss Encourages slots to align with consistent 3D […]<\/p>\n","protected":false},"author":280,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-13","page","type-page","status-publish","hentry"],"yoast_head":"\nExperiments - Slot4D: Learning Object Slots for 4D Reconstruction<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf7\/experiments\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Experiments - Slot4D: Learning Object Slots for 4D Reconstruction\" \/>\n<meta property=\"og:description\" content=\"Baseline Removing the dropout from slot attention head improves performance Baseline fails at identifying smaller objects and have blur boundaries. 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