Near, far: Patch-ordering enhances vision foundation models' scene understanding (2408.11054v3)

Abstract: We introduce NeCo: Patch Neighbor Consistency, a novel self-supervised training loss that enforces patch-level nearest neighbor consistency across a student and teacher model. Compared to contrastive approaches that only yield binary learning signals, i.e., 'attract' and 'repel', this approach benefits from the more fine-grained learning signal of sorting spatially dense features relative to reference patches. Our method leverages differentiable sorting applied on top of pretrained representations, such as DINOv2-registers to bootstrap the learning signal and further improve upon them. This dense post-pretraining leads to superior performance across various models and datasets, despite requiring only 19 hours on a single GPU. This method generates high-quality dense feature encoders and establishes several new state-of-the-art results such as +5.5% and +6% for non-parametric in-context semantic segmentation on ADE20k and Pascal VOC, +7.2% and +5.7% for linear segmentation evaluations on COCO-Things and -Stuff and improvements in the 3D understanding of multi-view consistency on SPair-71k, by more than 1.5%.

• The paper introduces NeCo, a novel self-supervised method that enforces patch-level consistency to improve DINOv2’s spatial representations.
• It leverages a differentiable sorting mechanism and patch matching to yield significant gains, including up to a 7.2% increase in linear segmentation on COCO-Things.
• The approach is highly efficient, requiring only 19 GPU hours and proving compatible with various pretrained backbones to advance dense prediction tasks.

Improving DINOv2's Spatial Representations Using NeCo: An Efficient Approach with Patch Neighbor Consistency

The paper "Improving DINOv2's spatial representations in 19 GPU hours with Patch Neighbor Consistency" introduces a method called NeCo, emphasizing its capacity to significantly enhance spatial representations in vision transformers, particularly the pre-trained DINOv2 model. The method leverages patch representations across views as a self-supervised signal to improve feature extractors without supervision. This research delineates several contributions and implications that are pertinent to the domain of dense self-supervised learning, focusing on the spatial consistency of patch-level features.

Core Contributions

The paper emphasizes a novel training loss function, Patch Neighbor Consistency (NeCo), designed to enforce nearest neighbor consistency between teacher and student model representations across different views. This is achieved by utilizing a differentiable sorting mechanism applied on top of a pretrained model's representations. The training process is notably efficient, with modifications taking only 19 hours on a single GPU, yet leading to superior performance across a variety of models and datasets. Empirically, several new state-of-the-art results are demonstrated, including improvements in non-parametric and linear segmentation tasks on datasets like ADE20k, Pascal VOC, and COCO.

Numerical Results and Performance Metrics

The research presents substantial quantitative evidence of improvement:

• A 5.5% and 6% increase in non-parametric in-context semantic segmentation on ADE20k and Pascal VOC, respectively.
• A 7.2% and 5.7% rise in linear segmentation evaluations on COCO-Things and COCO-Stuff.

These results underscore the efficiency and effectiveness of NeCo in creating highly valuable dense feature encoders that optimize spatial representation tasks. The method's low computational cost relative to the gained performance is highlighted as a pivotal feature.

Theoretical and Practical Implications

Theoretically, NeCo bridges the gap between image-level and patch-level self-supervised learning by integrating nearest-neighbor relationships into model training. This approach innovatively enhances the clustering effect of features at a granularity that considers both object parts and their spatial arrangement, which are critical for tasks requiring detailed scene understanding. Practically, by adapting existing models such as DINOv2 for enhanced in-context learning, NeCo provides a framework that can be incorporated into various applications, from semantic segmentation to object-centric learning challenges.

Compatibility and Enhancement of Existing Methods

A noteworthy aspect of the NeCo approach is its compatibility with various pretrained backbones, proving its potential for generalization. The method was applied to six different foundations, reinforcing its flexibility and adaptability in enhancing pretrained vision transformers' dense representation capabilities.

Future Directions and Speculations

In advancing the field of AI, the NeCo framework opens pathways for exploring more context-aware feature extraction processes. By focusing on sophisticated self-supervised signals, future studies may continue to refine the granularity of self-supervised learning, fostering advancements in tasks beyond traditional semantic segmentation. Additionally, practical applications could benefit from a model's ability to adapt quickly to diverse datasets and tasks with minimal computational resources.

The proposal and implementation of NeCo demonstrate a valuable contribution to the domain of computer vision and self-supervised learning, particularly for applications demanding intricate spatial reasoning and efficient model adaptation. This paper paves the way for further exploration on how patch-level consistency can optimize neural networks for complex, dense prediction tasks.