[2511.05168] Another BRIXEL in the Wall: Towards Cheaper Dense Features

Computer Science > Computer Vision and Pattern Recognition arXiv:2511.05168 (cs) [Submitted on 7 Nov 2025 (v1), last revised 10 Apr 2026 (this version, v2)] Title:Another BRIXEL in the Wall: Towards Cheaper Dense Features Authors:Alexander Lappe, Martin A. Giese View a PDF of the paper titled Another BRIXEL in the Wall: Towards Cheaper Dense Features, by Alexander Lappe and Martin A. Giese View PDF HTML (experimental) Abstract:Vision foundation models achieve strong performance on both global and locally dense downstream tasks. Pretrained on large images, the recent DINOv3 model family is able to produce very fine-grained dense feature maps, enabling state-of-the-art performance. However, computing these feature maps requires the input image to be available at very high resolution, as well as large amounts of compute due to the squared complexity of the transformer architecture. To address these issues, we propose BRIXEL, a simple knowledge distillation approach that has the student learn to reproduce its own feature maps at higher resolution. Despite its simplicity, BRIXEL outperforms the baseline DINOv3 models by large margins on downstream tasks when the resolution is kept fixed. We also apply BRIXEL to other recent dense-feature extractors and show that it yields substantial performance gains across model families. Code and model weights are available at this https URL. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2...