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A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning

A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning (CVPR 2025)
By Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi.

Introduction

framework

An overview of this paper. (a) We conduct a comprehensive study evaluating pre-trained vision models (PVMs) on visuomotor control and perception tasks, analyzing how different pretraining (model, data) combinations affect performance. Our analysis reveals that DINO/iBOT excels while MAE underperforms. (b) We investigate the performance drop of DINO/iBOT when trained on non-(single-)object-centric (NOC) data, discovering they struggle to learn objectness from NOC data—a capability that strongly correlates with robot manipulation performance. (c) We introduce SlotMIM, which incorporates explicit objectness guidance during training to effectively learn object-centric representations from NOC data. (d) Through scaled-up pre-training and evaluation across six tasks, we demonstrate that SlotMIM adaptively learns different types of objectness based on the pre-training dataset characteristics, outperforming existing methods.

Getting started

Requirements

The following is an example of setting up the experimental environment:

  • Create the environment
conda create -n slotmim python=3.9 -y
conda activate slotmim
  • Install pytorch & torchvision (you can also pick your favorite version)
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
  • Clone our repo
git clone https://github.com/CVMI-Lab/SlotMIM &&cd ./SlotMIM
  • (Optional) Create a soft link for the datasets
mkdir datasets
ln -s ${PATH_TO_COCO} ./datasets/coco
ln -s ${PATH_TO_IMAGENET} ./datasets/imagenet

At this stage, we have provided the code for pre-training SlotMIM, and evaluation scripts for object discovery, classification, object detection, and segmentation. For training please check ./scripts/, and for evaluation please check ./transfer/ and eval_voc.py and eval_knn.py. We also have released pre-trained checkpoints of our model and other re-implemented baselines here. Please feel free to explore them for now and we will continue to update the readme with more instructions, and integrate evaluation scripts for robotics tasks in the future.

Citing this work

If you find this repo useful for your research, please consider citing our paper:

@inproceedings{wen2022slotcon,
title={Self-Supervised Visual Representation Learning with Semantic Grouping},
author={Wen, Xin and Zhao, Bingchen and Zheng, Anlin and Zhang, Xiangyu and Qi, Xiaojuan},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}
@inproceedings{wen2025slotmim,
title={A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning},
author={Wen, Xin and Zhao, Bingchen and Chen, Yilun and Pang, Jiangmiao and Qi, Xiaojuan},
booktitle={CVPR},
year={2025}
}

Acknowledgment

Our codebase builds upon several existing publicly available codes. Specifically, we have modified and integrated the following repos into this project: iBOT, MAE, SlotCon, PixPro, DINO, and Slot Attention.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

About

(CVPR 2025) A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning

arxiv.org/abs/2503.06960

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning

A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning (CVPR 2025)

By
Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi.

Introduction
framework
An overview of this paper. (a) We conduct a comprehensive study evaluating pre-trained vision models (PVMs) on visuomotor control and perception tasks, analyzing how different pretraining (model, data) combinations affect performance. Our analysis reveals that DINO/iBOT excels while MAE underperforms.
(b) We investigate the performance drop of DINO/iBOT when trained on non-(single-)object-centric (NOC) data, discovering they struggle to learn objectness from NOC data—a capability that strongly correlates with robot manipulation performance.
(c) We introduce SlotMIM, which incorporates explicit objectness guidance during training to effectively learn object-centric representations from NOC data.
(d) Through scaled-up pre-training and evaluation across six tasks, we demonstrate that SlotMIM adaptively learns different types of objectness based on the pre-training dataset characteristics, outperforming existing methods.
Getting started
Requirements
The following is an example of setting up the experimental environment:
  • Create the environment
conda create -n slotmim python=3.9 -y
conda activate slotmim
  • Install pytorch & torchvision (you can also pick your favorite version)
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
  • Clone our repo
git clone https://github.com/CVMI-Lab/SlotMIM &&cd ./SlotMIM
  • (Optional) Create a soft link for the datasets
mkdir datasets
ln -s ${PATH_TO_COCO} ./datasets/coco
ln -s ${PATH_TO_IMAGENET} ./datasets/imagenet
At this stage, we have provided the code for pre-training SlotMIM, and evaluation scripts for object discovery, classification, object detection, and segmentation. For training please check ./scripts/, and for evaluation please check ./transfer/ and eval_voc.py and eval_knn.py. We also have released pre-trained checkpoints of our model and other re-implemented baselines here.
Please feel free to explore them for now and we will continue to update the readme with more instructions, and integrate evaluation scripts for robotics tasks in the future.
Citing this work
If you find this repo useful for your research, please consider citing our paper:
@inproceedings{wen2022slotcon,
title={Self-Supervised Visual Representation Learning with Semantic Grouping},
author={Wen, Xin and Zhao, Bingchen and Zheng, Anlin and Zhang, Xiangyu and Qi, Xiaojuan},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}
@inproceedings{wen2025slotmim,
title={A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning},
author={Wen, Xin and Zhao, Bingchen and Chen, Yilun and Pang, Jiangmiao and Qi, Xiaojuan},
booktitle={CVPR},
year={2025}
}

Acknowledgment
Our codebase builds upon several existing publicly available codes. Specifically, we have modified and integrated the following repos into this project: iBOT, MAE, SlotCon, PixPro, DINO, and Slot Attention.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
About
(CVPR 2025) A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
arxiv.org/abs/2503.06960
Resources
Readme
Apache-2.0 license
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25 stars
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2 watching
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2 forks
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, '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");
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning

A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning (CVPR 2025)

By
Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi.

Introduction
framework
An overview of this paper. (a) We conduct a comprehensive study evaluating pre-trained vision models (PVMs) on visuomotor control and perception tasks, analyzing how different pretraining (model, data) combinations affect performance. Our analysis reveals that DINO/iBOT excels while MAE underperforms.
(b) We investigate the performance drop of DINO/iBOT when trained on non-(single-)object-centric (NOC) data, discovering they struggle to learn objectness from NOC data—a capability that strongly correlates with robot manipulation performance.
(c) We introduce SlotMIM, which incorporates explicit objectness guidance during training to effectively learn object-centric representations from NOC data.
(d) Through scaled-up pre-training and evaluation across six tasks, we demonstrate that SlotMIM adaptively learns different types of objectness based on the pre-training dataset characteristics, outperforming existing methods.
Getting started
Requirements
The following is an example of setting up the experimental environment:
  • Create the environment
conda create -n slotmim python=3.9 -y
conda activate slotmim
  • Install pytorch & torchvision (you can also pick your favorite version)
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
  • Clone our repo
git clone https://github.com/CVMI-Lab/SlotMIM &&cd ./SlotMIM
  • (Optional) Create a soft link for the datasets
mkdir datasets
ln -s ${PATH_TO_COCO} ./datasets/coco
ln -s ${PATH_TO_IMAGENET} ./datasets/imagenet
At this stage, we have provided the code for pre-training SlotMIM, and evaluation scripts for object discovery, classification, object detection, and segmentation. For training please check ./scripts/, and for evaluation please check ./transfer/ and eval_voc.py and eval_knn.py. We also have released pre-trained checkpoints of our model and other re-implemented baselines here.
Please feel free to explore them for now and we will continue to update the readme with more instructions, and integrate evaluation scripts for robotics tasks in the future.
Citing this work
If you find this repo useful for your research, please consider citing our paper:
@inproceedings{wen2022slotcon,
title={Self-Supervised Visual Representation Learning with Semantic Grouping},
author={Wen, Xin and Zhao, Bingchen and Zheng, Anlin and Zhang, Xiangyu and Qi, Xiaojuan},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}
@inproceedings{wen2025slotmim,
title={A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning},
author={Wen, Xin and Zhao, Bingchen and Chen, Yilun and Pang, Jiangmiao and Qi, Xiaojuan},
booktitle={CVPR},
year={2025}
}

Acknowledgment
Our codebase builds upon several existing publicly available codes. Specifically, we have modified and integrated the following repos into this project: iBOT, MAE, SlotCon, PixPro, DINO, and Slot Attention.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
About
(CVPR 2025) A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
arxiv.org/abs/2503.06960
Resources
Readme
Apache-2.0 license
Activity
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25 stars
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2 watching
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2 forks
Report repository
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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('^' + ".*" + '

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A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning

A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning (CVPR 2025)

By
Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi.

Introduction
framework
An overview of this paper. (a) We conduct a comprehensive study evaluating pre-trained vision models (PVMs) on visuomotor control and perception tasks, analyzing how different pretraining (model, data) combinations affect performance. Our analysis reveals that DINO/iBOT excels while MAE underperforms.
(b) We investigate the performance drop of DINO/iBOT when trained on non-(single-)object-centric (NOC) data, discovering they struggle to learn objectness from NOC data—a capability that strongly correlates with robot manipulation performance.
(c) We introduce SlotMIM, which incorporates explicit objectness guidance during training to effectively learn object-centric representations from NOC data.
(d) Through scaled-up pre-training and evaluation across six tasks, we demonstrate that SlotMIM adaptively learns different types of objectness based on the pre-training dataset characteristics, outperforming existing methods.
Getting started
Requirements
The following is an example of setting up the experimental environment:
  • Create the environment
conda create -n slotmim python=3.9 -y
conda activate slotmim
  • Install pytorch & torchvision (you can also pick your favorite version)
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
  • Clone our repo
git clone https://github.com/CVMI-Lab/SlotMIM &&cd ./SlotMIM
  • (Optional) Create a soft link for the datasets
mkdir datasets
ln -s ${PATH_TO_COCO} ./datasets/coco
ln -s ${PATH_TO_IMAGENET} ./datasets/imagenet
At this stage, we have provided the code for pre-training SlotMIM, and evaluation scripts for object discovery, classification, object detection, and segmentation. For training please check ./scripts/, and for evaluation please check ./transfer/ and eval_voc.py and eval_knn.py. We also have released pre-trained checkpoints of our model and other re-implemented baselines here.
Please feel free to explore them for now and we will continue to update the readme with more instructions, and integrate evaluation scripts for robotics tasks in the future.
Citing this work
If you find this repo useful for your research, please consider citing our paper:
@inproceedings{wen2022slotcon,
title={Self-Supervised Visual Representation Learning with Semantic Grouping},
author={Wen, Xin and Zhao, Bingchen and Zheng, Anlin and Zhang, Xiangyu and Qi, Xiaojuan},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}
@inproceedings{wen2025slotmim,
title={A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning},
author={Wen, Xin and Zhao, Bingchen and Chen, Yilun and Pang, Jiangmiao and Qi, Xiaojuan},
booktitle={CVPR},
year={2025}
}

Acknowledgment
Our codebase builds upon several existing publicly available codes. Specifically, we have modified and integrated the following repos into this project: iBOT, MAE, SlotCon, PixPro, DINO, and Slot Attention.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
About
(CVPR 2025) A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
arxiv.org/abs/2503.06960
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
25 stars
Watchers
2 watching
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2 forks
Report repository
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Contributors
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, '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");
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning

A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning (CVPR 2025)

By
Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi.

Introduction
framework
An overview of this paper. (a) We conduct a comprehensive study evaluating pre-trained vision models (PVMs) on visuomotor control and perception tasks, analyzing how different pretraining (model, data) combinations affect performance. Our analysis reveals that DINO/iBOT excels while MAE underperforms.
(b) We investigate the performance drop of DINO/iBOT when trained on non-(single-)object-centric (NOC) data, discovering they struggle to learn objectness from NOC data—a capability that strongly correlates with robot manipulation performance.
(c) We introduce SlotMIM, which incorporates explicit objectness guidance during training to effectively learn object-centric representations from NOC data.
(d) Through scaled-up pre-training and evaluation across six tasks, we demonstrate that SlotMIM adaptively learns different types of objectness based on the pre-training dataset characteristics, outperforming existing methods.
Getting started
Requirements
The following is an example of setting up the experimental environment:
  • Create the environment
conda create -n slotmim python=3.9 -y
conda activate slotmim
  • Install pytorch & torchvision (you can also pick your favorite version)
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
  • Clone our repo
git clone https://github.com/CVMI-Lab/SlotMIM &&cd ./SlotMIM
  • (Optional) Create a soft link for the datasets
mkdir datasets
ln -s ${PATH_TO_COCO} ./datasets/coco
ln -s ${PATH_TO_IMAGENET} ./datasets/imagenet
At this stage, we have provided the code for pre-training SlotMIM, and evaluation scripts for object discovery, classification, object detection, and segmentation. For training please check ./scripts/, and for evaluation please check ./transfer/ and eval_voc.py and eval_knn.py. We also have released pre-trained checkpoints of our model and other re-implemented baselines here.
Please feel free to explore them for now and we will continue to update the readme with more instructions, and integrate evaluation scripts for robotics tasks in the future.
Citing this work
If you find this repo useful for your research, please consider citing our paper:
@inproceedings{wen2022slotcon,
title={Self-Supervised Visual Representation Learning with Semantic Grouping},
author={Wen, Xin and Zhao, Bingchen and Zheng, Anlin and Zhang, Xiangyu and Qi, Xiaojuan},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}
@inproceedings{wen2025slotmim,
title={A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning},
author={Wen, Xin and Zhao, Bingchen and Chen, Yilun and Pang, Jiangmiao and Qi, Xiaojuan},
booktitle={CVPR},
year={2025}
}

Acknowledgment
Our codebase builds upon several existing publicly available codes. Specifically, we have modified and integrated the following repos into this project: iBOT, MAE, SlotCon, PixPro, DINO, and Slot Attention.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
About
(CVPR 2025) A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
arxiv.org/abs/2503.06960
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
25 stars
Watchers
2 watching
Forks
2 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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning

A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning (CVPR 2025)

By
Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi.

Introduction
framework
An overview of this paper. (a) We conduct a comprehensive study evaluating pre-trained vision models (PVMs) on visuomotor control and perception tasks, analyzing how different pretraining (model, data) combinations affect performance. Our analysis reveals that DINO/iBOT excels while MAE underperforms.
(b) We investigate the performance drop of DINO/iBOT when trained on non-(single-)object-centric (NOC) data, discovering they struggle to learn objectness from NOC data—a capability that strongly correlates with robot manipulation performance.
(c) We introduce SlotMIM, which incorporates explicit objectness guidance during training to effectively learn object-centric representations from NOC data.
(d) Through scaled-up pre-training and evaluation across six tasks, we demonstrate that SlotMIM adaptively learns different types of objectness based on the pre-training dataset characteristics, outperforming existing methods.
Getting started
Requirements
The following is an example of setting up the experimental environment:
  • Create the environment
conda create -n slotmim python=3.9 -y
conda activate slotmim
  • Install pytorch & torchvision (you can also pick your favorite version)
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
  • Clone our repo
git clone https://github.com/CVMI-Lab/SlotMIM &&cd ./SlotMIM
  • (Optional) Create a soft link for the datasets
mkdir datasets
ln -s ${PATH_TO_COCO} ./datasets/coco
ln -s ${PATH_TO_IMAGENET} ./datasets/imagenet
At this stage, we have provided the code for pre-training SlotMIM, and evaluation scripts for object discovery, classification, object detection, and segmentation. For training please check ./scripts/, and for evaluation please check ./transfer/ and eval_voc.py and eval_knn.py. We also have released pre-trained checkpoints of our model and other re-implemented baselines here.
Please feel free to explore them for now and we will continue to update the readme with more instructions, and integrate evaluation scripts for robotics tasks in the future.
Citing this work
If you find this repo useful for your research, please consider citing our paper:
@inproceedings{wen2022slotcon,
title={Self-Supervised Visual Representation Learning with Semantic Grouping},
author={Wen, Xin and Zhao, Bingchen and Zheng, Anlin and Zhang, Xiangyu and Qi, Xiaojuan},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}
@inproceedings{wen2025slotmim,
title={A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning},
author={Wen, Xin and Zhao, Bingchen and Chen, Yilun and Pang, Jiangmiao and Qi, Xiaojuan},
booktitle={CVPR},
year={2025}
}

Acknowledgment
Our codebase builds upon several existing publicly available codes. Specifically, we have modified and integrated the following repos into this project: iBOT, MAE, SlotCon, PixPro, DINO, and Slot Attention.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
About
(CVPR 2025) A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
arxiv.org/abs/2503.06960
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, '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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning

A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning (CVPR 2025)

By
Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi.

Introduction
framework
An overview of this paper. (a) We conduct a comprehensive study evaluating pre-trained vision models (PVMs) on visuomotor control and perception tasks, analyzing how different pretraining (model, data) combinations affect performance. Our analysis reveals that DINO/iBOT excels while MAE underperforms.
(b) We investigate the performance drop of DINO/iBOT when trained on non-(single-)object-centric (NOC) data, discovering they struggle to learn objectness from NOC data—a capability that strongly correlates with robot manipulation performance.
(c) We introduce SlotMIM, which incorporates explicit objectness guidance during training to effectively learn object-centric representations from NOC data.
(d) Through scaled-up pre-training and evaluation across six tasks, we demonstrate that SlotMIM adaptively learns different types of objectness based on the pre-training dataset characteristics, outperforming existing methods.
Getting started
Requirements
The following is an example of setting up the experimental environment:
  • Create the environment
conda create -n slotmim python=3.9 -y
conda activate slotmim
  • Install pytorch & torchvision (you can also pick your favorite version)
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
  • Clone our repo
git clone https://github.com/CVMI-Lab/SlotMIM &&cd ./SlotMIM
  • (Optional) Create a soft link for the datasets
mkdir datasets
ln -s ${PATH_TO_COCO} ./datasets/coco
ln -s ${PATH_TO_IMAGENET} ./datasets/imagenet
At this stage, we have provided the code for pre-training SlotMIM, and evaluation scripts for object discovery, classification, object detection, and segmentation. For training please check ./scripts/, and for evaluation please check ./transfer/ and eval_voc.py and eval_knn.py. We also have released pre-trained checkpoints of our model and other re-implemented baselines here.
Please feel free to explore them for now and we will continue to update the readme with more instructions, and integrate evaluation scripts for robotics tasks in the future.
Citing this work
If you find this repo useful for your research, please consider citing our paper:
@inproceedings{wen2022slotcon,
title={Self-Supervised Visual Representation Learning with Semantic Grouping},
author={Wen, Xin and Zhao, Bingchen and Zheng, Anlin and Zhang, Xiangyu and Qi, Xiaojuan},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}
@inproceedings{wen2025slotmim,
title={A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning},
author={Wen, Xin and Zhao, Bingchen and Chen, Yilun and Pang, Jiangmiao and Qi, Xiaojuan},
booktitle={CVPR},
year={2025}
}

Acknowledgment
Our codebase builds upon several existing publicly available codes. Specifically, we have modified and integrated the following repos into this project: iBOT, MAE, SlotCon, PixPro, DINO, and Slot Attention.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
About
(CVPR 2025) A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
arxiv.org/abs/2503.06960
Resources
Readme
Apache-2.0 license
Activity
Custom properties
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25 stars
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2 watching
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2 forks
Report repository
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, '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); }
})();
})();

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A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning

A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning (CVPR 2025)

By
Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi.

Introduction
framework
An overview of this paper. (a) We conduct a comprehensive study evaluating pre-trained vision models (PVMs) on visuomotor control and perception tasks, analyzing how different pretraining (model, data) combinations affect performance. Our analysis reveals that DINO/iBOT excels while MAE underperforms.
(b) We investigate the performance drop of DINO/iBOT when trained on non-(single-)object-centric (NOC) data, discovering they struggle to learn objectness from NOC data—a capability that strongly correlates with robot manipulation performance.
(c) We introduce SlotMIM, which incorporates explicit objectness guidance during training to effectively learn object-centric representations from NOC data.
(d) Through scaled-up pre-training and evaluation across six tasks, we demonstrate that SlotMIM adaptively learns different types of objectness based on the pre-training dataset characteristics, outperforming existing methods.
Getting started
Requirements
The following is an example of setting up the experimental environment:
  • Create the environment
conda create -n slotmim python=3.9 -y
conda activate slotmim
  • Install pytorch & torchvision (you can also pick your favorite version)
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
  • Clone our repo
git clone https://github.com/CVMI-Lab/SlotMIM &&cd ./SlotMIM
  • (Optional) Create a soft link for the datasets
mkdir datasets
ln -s ${PATH_TO_COCO} ./datasets/coco
ln -s ${PATH_TO_IMAGENET} ./datasets/imagenet
At this stage, we have provided the code for pre-training SlotMIM, and evaluation scripts for object discovery, classification, object detection, and segmentation. For training please check ./scripts/, and for evaluation please check ./transfer/ and eval_voc.py and eval_knn.py. We also have released pre-trained checkpoints of our model and other re-implemented baselines here.
Please feel free to explore them for now and we will continue to update the readme with more instructions, and integrate evaluation scripts for robotics tasks in the future.
Citing this work
If you find this repo useful for your research, please consider citing our paper:
@inproceedings{wen2022slotcon,
title={Self-Supervised Visual Representation Learning with Semantic Grouping},
author={Wen, Xin and Zhao, Bingchen and Zheng, Anlin and Zhang, Xiangyu and Qi, Xiaojuan},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}
@inproceedings{wen2025slotmim,
title={A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning},
author={Wen, Xin and Zhao, Bingchen and Chen, Yilun and Pang, Jiangmiao and Qi, Xiaojuan},
booktitle={CVPR},
year={2025}
}

Acknowledgment
Our codebase builds upon several existing publicly available codes. Specifically, we have modified and integrated the following repos into this project: iBOT, MAE, SlotCon, PixPro, DINO, and Slot Attention.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
About
(CVPR 2025) A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
arxiv.org/abs/2503.06960
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
25 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages