In Pursuit of Pixel Supervision for Visual Pre-training
At the most basic level, pixels are the source of the visual information through which we perceive the world. Pixels contain information at all levels, ranging from low-level attributes to high-level concepts. Autoencoders represent a classical and long-standing paradigm for learning representations from pixels or other raw inputs. In this work, we demonstrate that autoencoder-based self-supervised learning remains competitive today and can produce strong representations for downstream tasks, while remaining simple, stable, and efficient. Our model, codenamed "Pixio", is an enhanced masked autoencoder (MAE) with more challenging pre-training tasks and more capable architectures. The model is trained on 2B web-crawled images with a self-curation strategy with minimal human curation. Pixio performs competitively across a wide range of downstream tasks in the wild, including monocular depth estimation (e.g., Depth Anything), feed-forward 3D reconstruction (i.e., MapAnything), semantic segmentation, and robot learning, outperforming or matching DINOv3 trained at similar scales. Our results suggest that pixel-space self-supervised learning can serve as a promising alternative and a complement to latent-space approaches.
694370f5542d62d58a7bf6a0
Lihe Yang
694370f5542d62d58a7bf6a1
Shang-Wen Li
claimed_verified
2025-12-19T09:07:16.497Z
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Shang-Wen Daniel Li
swdanielli
user
swdanielli
694370f5542d62d58a7bf6a2
Yang Li
694370f5542d62d58a7bf6a3
Xinjie Lei
694370f5542d62d58a7bf6a4
Dong Wang
694370f5542d62d58a7bf6a5
Abdelrahman Mohamed
694370f5542d62d58a7bf6a6
Hengshuang Zhao
694370f5542d62d58a7bf6a7
Hu Xu
In Pursuit of Pixel Supervision for Visual Pre-training
At the most basic level, pixels are the source of the visual information through which we perceive the world. Pixels contain information at all levels, ranging from low-level attributes to high-level concepts. Autoencoders represent a classical and long-standing paradigm for learning representations from pixels or other raw inputs. In this work, we demonstrate that autoencoder-based self-supervised learning remains competitive today and can produce strong representations for downstream tasks, while remaining simple, stable, and efficient. Our model, codenamed "Pixio", is an enhanced masked autoencoder (MAE) with more challenging pre-training tasks and more capable architectures. The model is trained on 2B web-crawled images with a self-curation strategy with minimal human curation. Pixio performs competitively across a wide range of downstream tasks in the wild, including monocular depth estimation (e.g., Depth Anything), feed-forward 3D reconstruction (i.e., MapAnything), semantic segmentation, and robot learning, outperforming or matching DINOv3 trained at similar scales. Our results suggest that pixel-space self-supervised learning can serve as a promising alternative and a complement to latent-space approaches.
Pixel-space self-supervised learning using an enhanced masked autoencoder achieves competitive performance across diverse downstream tasks while maintaining simplicity and efficiency.
autoencoders self-supervised learning masked autoencoder MAE pixel space latent space downstream tasks Depth Anything MapAnything semantic segmentation robot learning DINOv3