arXiv cs.AI· Ulysse Mizrahi, Sean Zaretzky, Dov Danon, Amit H. Bermano·· 1 天前AI 评分26
ElasticDino：用弹性变形生成密集语义特征
ElasticDino: Dense Semantic Features Using Elastic Deformations
AI 导读
ElasticDino 是一个轻量适配器，可为 ViT 生成像素对齐的高分辨率特征图。它利用基于空间变形的精简架构，同时消除跨通道一致的伪影，避免其他方法固有的平滑偏差。在多个密集任务基准上，其精度优于现有方法且计算性能更好。
正文 · 原文
Abstract:Vision transformers (ViTs) serve as the backbone for countless vision tasks, including dense prediction tasks, such as segmentation and depth estimation, even though the quadratic complexity of self-attention severely limits feature resolution and induces spatial artifacts. In this work, we introduce ElasticDino, a lightweight adapter which produces high-resolution feature maps, adhering to the fine details of the input image. As a key insight, we observe the consistency of artifacts across channels, and simultaneously resolve them to match the input image structure. Using a slim architecture based on spatial deformations, ElasticDino produces pixel-aligned, high-resolution feature maps efficiently. Leveraging deformations allows strictly preserving sharp semantics, and thereby avoiding the smoothness biases inherent to alternative approaches. Due to the absence of ground truth, we employ pixel-level semantic segmentation and depth estimation with linear heads to predict a set of downstream dense tasks, guiding the deformation process without leaking task-specific semantics into the model. We demonstrate superior accuracy over current methods on a range of popular dense task benchmarks, with better computational performance.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.16984 [cs.CV] |
| (or arXiv:2611.16984v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16984 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ulysse Mizrahi [view email]
[v1]
Thu, 12 Nov 2026 15:50:04 UTC (9,294 KB)
来源：arXiv cs.AI · arxiv.org