Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation

2025-05-04

AI Summary

Low-resolution features from Vision Foundation Models (VFMs) hinder dense prediction tasks—particularly Interactive Segmentation (IS)—due to insufficient spatial fidelity. Method: This paper introduces IS as a novel, rigorous, and task-agnostic benchmark for systematically evaluating feature upsampling techniques. For the first time, IS—mapping multimodal click inputs to pixel-level masks—is adopted as a unified evaluation platform to quantitatively assess how upsampling strategies affect VFM feature quality. Within a consistent end-to-end framework, we integrate and evaluate interpolation, transposed convolution, and attention-enhanced modules for upsampling VFM backbone features. Results: Empirical analysis demonstrates that well-designed upsampling significantly improves IS performance. We release iSegProbe, an open-source toolkit that provides standardized evaluation and development support for adapting VFMs to dense prediction tasks.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Vision Foundation Models (VFMs) are large-scale, pre-trained models that serve as general-purpose backbones for various computer vision tasks. As VFMs' popularity grows, there is an increasing interest in understanding their effectiveness for dense prediction tasks. However, VFMs typically produce low-resolution features, limiting their direct applicability in this context. One way to tackle this limitation is by employing a task-agnostic feature upsampling module that refines VFM features resolution. To assess the effectiveness of this approach, we investigate Interactive Segmentation (IS) as a novel benchmark for evaluating feature upsampling methods on VFMs. Due to its inherent multimodal input, consisting of an image and a set of user-defined clicks, as well as its dense mask output, IS creates a challenging environment that demands comprehensive visual scene understanding. Our benchmarking experiments show that selecting appropriate upsampling strategies significantly improves VFM features quality. The code is released at https://github.com/havrylovv/iSegProbe

Problem

• Evaluating feature upsampling methods for Vision Foundation Models
• Assessing upsampling impact on dense prediction tasks
• Benchmarking upsampling strategies via Interactive Segmentation

Innovation

• Task-agnostic feature upsampling for VFMs
• Interactive Segmentation as benchmark method
• Upsampling strategies enhance VFM feature quality