BranchesTags

Repository files navigation

Rotating Features for Object Discovery

Our proposed Rotating Features learn to represent object affiliation via their orientation on real-world data without labels. We achieve this by extending standard features by an extra dimension across the entire architecture. We then set up the layer structure in such a way that the Rotating Features’ magnitudes learn to represent the presence of features, while their orientations learn to represent object affiliation. This allows us to achieve strong object discovery performance on real-world images in an unsupervised way.

This repository contains the code for the paper Rotating Features for Object Discovery by Sindy Löwe, Phillip Lippe, Francesco Locatello and Max Welling.

Rotating Features for Object Discovery

Setup

  • Install conda
  • Adjust the setup_environment.sh script to your needs (e.g. by setting the right CUDA version)
  • Run the setup script:
bash setup_environment.sh
  • Run the download script to download the 4Shapes datasets, FoodSeg103 and Pascal VOC 2012:
bash download_data.sh

Run Experiments

  • Train and evaluate an autoencoding model with Rotating Features on the provided datasets:
source activate RotatingFeatures
python -m codebase.main +experiment=4Shapes
python -m codebase.main +experiment=4Shapes_RGBD
python -m codebase.main +experiment=FoodSeg
python -m codebase.main +experiment=Pascal

Citation

When using this code, please cite our paper:

@article{lowe2023rotating,
title={Rotating Features for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Locatello, Francesco and Welling, Max},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2023}
}

Contact

For questions and suggestions, feel free to open an issue on GitHub or send an email to loewe.sindy@gmail.com.

About

Code for the paper: Rotating Features for Object Discovery

Resources

Readme
MIT license
Activity

Stars

53 stars

Watchers

1 watching

Forks

5 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Rotating Features for Object Discovery
Our proposed Rotating Features learn to represent object affiliation via their orientation on real-world data without labels.
We achieve this by extending standard features by an extra dimension across the entire architecture.
We then set up the layer structure in such a way that the Rotating Features’ magnitudes learn to represent the presence
of features, while their orientations learn to represent object affiliation.
This allows us to achieve strong object discovery performance on real-world images in an unsupervised way.
This repository contains the code for the
paper Rotating Features for Object Discovery by Sindy Löwe, Phillip Lippe, Francesco
Locatello and Max Welling.
Rotating Features for Object Discovery
Setup
  • Install conda
  • Adjust the setup_environment.sh script to your needs (e.g. by setting the right CUDA version)
  • Run the setup script:
bash setup_environment.sh
  • Run the download script to download the 4Shapes datasets, FoodSeg103 and Pascal VOC 2012:
bash download_data.sh
Run Experiments
  • Train and evaluate an autoencoding model with Rotating Features on the provided datasets:
source activate RotatingFeatures
python -m codebase.main +experiment=4Shapes
python -m codebase.main +experiment=4Shapes_RGBD
python -m codebase.main +experiment=FoodSeg
python -m codebase.main +experiment=Pascal
Citation
When using this code, please cite our paper:
@article{lowe2023rotating,
title={Rotating Features for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Locatello, Francesco and Welling, Max},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2023}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email
to loewe.sindy@gmail.com.
About
Code for the paper: Rotating Features for Object Discovery
Resources
Readme
MIT license
Activity
Stars
53 stars
Watchers
1 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Rotating Features for Object Discovery
Our proposed Rotating Features learn to represent object affiliation via their orientation on real-world data without labels.
We achieve this by extending standard features by an extra dimension across the entire architecture.
We then set up the layer structure in such a way that the Rotating Features’ magnitudes learn to represent the presence
of features, while their orientations learn to represent object affiliation.
This allows us to achieve strong object discovery performance on real-world images in an unsupervised way.
This repository contains the code for the
paper Rotating Features for Object Discovery by Sindy Löwe, Phillip Lippe, Francesco
Locatello and Max Welling.
Rotating Features for Object Discovery
Setup
  • Install conda
  • Adjust the setup_environment.sh script to your needs (e.g. by setting the right CUDA version)
  • Run the setup script:
bash setup_environment.sh
  • Run the download script to download the 4Shapes datasets, FoodSeg103 and Pascal VOC 2012:
bash download_data.sh
Run Experiments
  • Train and evaluate an autoencoding model with Rotating Features on the provided datasets:
source activate RotatingFeatures
python -m codebase.main +experiment=4Shapes
python -m codebase.main +experiment=4Shapes_RGBD
python -m codebase.main +experiment=FoodSeg
python -m codebase.main +experiment=Pascal
Citation
When using this code, please cite our paper:
@article{lowe2023rotating,
title={Rotating Features for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Locatello, Francesco and Welling, Max},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2023}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email
to loewe.sindy@gmail.com.
About
Code for the paper: Rotating Features for Object Discovery
Resources
Readme
MIT license
Activity
Stars
53 stars
Watchers
1 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Rotating Features for Object Discovery
Our proposed Rotating Features learn to represent object affiliation via their orientation on real-world data without labels.
We achieve this by extending standard features by an extra dimension across the entire architecture.
We then set up the layer structure in such a way that the Rotating Features’ magnitudes learn to represent the presence
of features, while their orientations learn to represent object affiliation.
This allows us to achieve strong object discovery performance on real-world images in an unsupervised way.
This repository contains the code for the
paper Rotating Features for Object Discovery by Sindy Löwe, Phillip Lippe, Francesco
Locatello and Max Welling.
Rotating Features for Object Discovery
Setup
  • Install conda
  • Adjust the setup_environment.sh script to your needs (e.g. by setting the right CUDA version)
  • Run the setup script:
bash setup_environment.sh
  • Run the download script to download the 4Shapes datasets, FoodSeg103 and Pascal VOC 2012:
bash download_data.sh
Run Experiments
  • Train and evaluate an autoencoding model with Rotating Features on the provided datasets:
source activate RotatingFeatures
python -m codebase.main +experiment=4Shapes
python -m codebase.main +experiment=4Shapes_RGBD
python -m codebase.main +experiment=FoodSeg
python -m codebase.main +experiment=Pascal
Citation
When using this code, please cite our paper:
@article{lowe2023rotating,
title={Rotating Features for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Locatello, Francesco and Welling, Max},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2023}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email
to loewe.sindy@gmail.com.
About
Code for the paper: Rotating Features for Object Discovery
Resources
Readme
MIT license
Activity
Stars
53 stars
Watchers
1 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Rotating Features for Object Discovery
Our proposed Rotating Features learn to represent object affiliation via their orientation on real-world data without labels.
We achieve this by extending standard features by an extra dimension across the entire architecture.
We then set up the layer structure in such a way that the Rotating Features’ magnitudes learn to represent the presence
of features, while their orientations learn to represent object affiliation.
This allows us to achieve strong object discovery performance on real-world images in an unsupervised way.
This repository contains the code for the
paper Rotating Features for Object Discovery by Sindy Löwe, Phillip Lippe, Francesco
Locatello and Max Welling.
Rotating Features for Object Discovery
Setup
  • Install conda
  • Adjust the setup_environment.sh script to your needs (e.g. by setting the right CUDA version)
  • Run the setup script:
bash setup_environment.sh
  • Run the download script to download the 4Shapes datasets, FoodSeg103 and Pascal VOC 2012:
bash download_data.sh
Run Experiments
  • Train and evaluate an autoencoding model with Rotating Features on the provided datasets:
source activate RotatingFeatures
python -m codebase.main +experiment=4Shapes
python -m codebase.main +experiment=4Shapes_RGBD
python -m codebase.main +experiment=FoodSeg
python -m codebase.main +experiment=Pascal
Citation
When using this code, please cite our paper:
@article{lowe2023rotating,
title={Rotating Features for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Locatello, Francesco and Welling, Max},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2023}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email
to loewe.sindy@gmail.com.
About
Code for the paper: Rotating Features for Object Discovery
Resources
Readme
MIT license
Activity
Stars
53 stars
Watchers
1 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Rotating Features for Object Discovery
Our proposed Rotating Features learn to represent object affiliation via their orientation on real-world data without labels.
We achieve this by extending standard features by an extra dimension across the entire architecture.
We then set up the layer structure in such a way that the Rotating Features’ magnitudes learn to represent the presence
of features, while their orientations learn to represent object affiliation.
This allows us to achieve strong object discovery performance on real-world images in an unsupervised way.
This repository contains the code for the
paper Rotating Features for Object Discovery by Sindy Löwe, Phillip Lippe, Francesco
Locatello and Max Welling.
Rotating Features for Object Discovery
Setup
  • Install conda
  • Adjust the setup_environment.sh script to your needs (e.g. by setting the right CUDA version)
  • Run the setup script:
bash setup_environment.sh
  • Run the download script to download the 4Shapes datasets, FoodSeg103 and Pascal VOC 2012:
bash download_data.sh
Run Experiments
  • Train and evaluate an autoencoding model with Rotating Features on the provided datasets:
source activate RotatingFeatures
python -m codebase.main +experiment=4Shapes
python -m codebase.main +experiment=4Shapes_RGBD
python -m codebase.main +experiment=FoodSeg
python -m codebase.main +experiment=Pascal
Citation
When using this code, please cite our paper:
@article{lowe2023rotating,
title={Rotating Features for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Locatello, Francesco and Welling, Max},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2023}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email
to loewe.sindy@gmail.com.
About
Code for the paper: Rotating Features for Object Discovery
Resources
Readme
MIT license
Activity
Stars
53 stars
Watchers
1 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Rotating Features for Object Discovery
Our proposed Rotating Features learn to represent object affiliation via their orientation on real-world data without labels.
We achieve this by extending standard features by an extra dimension across the entire architecture.
We then set up the layer structure in such a way that the Rotating Features’ magnitudes learn to represent the presence
of features, while their orientations learn to represent object affiliation.
This allows us to achieve strong object discovery performance on real-world images in an unsupervised way.
This repository contains the code for the
paper Rotating Features for Object Discovery by Sindy Löwe, Phillip Lippe, Francesco
Locatello and Max Welling.
Rotating Features for Object Discovery
Setup
  • Install conda
  • Adjust the setup_environment.sh script to your needs (e.g. by setting the right CUDA version)
  • Run the setup script:
bash setup_environment.sh
  • Run the download script to download the 4Shapes datasets, FoodSeg103 and Pascal VOC 2012:
bash download_data.sh
Run Experiments
  • Train and evaluate an autoencoding model with Rotating Features on the provided datasets:
source activate RotatingFeatures
python -m codebase.main +experiment=4Shapes
python -m codebase.main +experiment=4Shapes_RGBD
python -m codebase.main +experiment=FoodSeg
python -m codebase.main +experiment=Pascal
Citation
When using this code, please cite our paper:
@article{lowe2023rotating,
title={Rotating Features for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Locatello, Francesco and Welling, Max},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2023}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email
to loewe.sindy@gmail.com.
About
Code for the paper: Rotating Features for Object Discovery
Resources
Readme
MIT license
Activity
Stars
53 stars
Watchers
1 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Rotating Features for Object Discovery
Our proposed Rotating Features learn to represent object affiliation via their orientation on real-world data without labels.
We achieve this by extending standard features by an extra dimension across the entire architecture.
We then set up the layer structure in such a way that the Rotating Features’ magnitudes learn to represent the presence
of features, while their orientations learn to represent object affiliation.
This allows us to achieve strong object discovery performance on real-world images in an unsupervised way.
This repository contains the code for the
paper Rotating Features for Object Discovery by Sindy Löwe, Phillip Lippe, Francesco
Locatello and Max Welling.
Rotating Features for Object Discovery
Setup
  • Install conda
  • Adjust the setup_environment.sh script to your needs (e.g. by setting the right CUDA version)
  • Run the setup script:
bash setup_environment.sh
  • Run the download script to download the 4Shapes datasets, FoodSeg103 and Pascal VOC 2012:
bash download_data.sh
Run Experiments
  • Train and evaluate an autoencoding model with Rotating Features on the provided datasets:
source activate RotatingFeatures
python -m codebase.main +experiment=4Shapes
python -m codebase.main +experiment=4Shapes_RGBD
python -m codebase.main +experiment=FoodSeg
python -m codebase.main +experiment=Pascal
Citation
When using this code, please cite our paper:
@article{lowe2023rotating,
title={Rotating Features for Object Discovery},
author={L{\"o}we, Sindy and Lippe, Phillip and Locatello, Francesco and Welling, Max},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2023}
}

Contact
For questions and suggestions, feel free to open an issue on GitHub or send an email
to loewe.sindy@gmail.com.
About
Code for the paper: Rotating Features for Object Discovery
Resources
Readme
MIT license
Activity
Stars
53 stars
Watchers
1 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages