Looking Locally: Object-Centric Vision Transformers as Foundation Models for Efficient Segmentation

TL;DR

FLIP is a biologically-inspired, object-centric vision transformer that efficiently segments objects by focusing high-resolution processing on object centers, outperforming larger models in accuracy and speed across multiple benchmarks.

Contribution

Introduces FLIP, a scale-invariant, resource-efficient vision model that uses top-down attention to improve object segmentation, especially for small objects, with significantly fewer parameters.

Findings

FLIP outperforms SAM models in IoU across benchmarks.

FLIP achieves high accuracy with over 1000x fewer parameters.

FLIP effectively segments very small objects in diverse scenes.

Abstract

Current state-of-the-art segmentation models encode entire images before focusing on specific objects. As a result, they waste computational resources - particularly when small objects are to be segmented in high-resolution scenes. We introduce FLIP (Fovea-Like Input Patching), a parameter-efficient vision model that realizes object segmentation through biologically-inspired top-down attention. FLIP selectively samples multi-resolution patches centered on objects of interest from the input. As a result, it allocates high-resolution processing to object centers while maintaining coarser peripheral context. This off-grid, scale-invariant design enables FLIP to outperform META's Segment Anything models (SAM) by large margins: With more than 1000x fewer parameters, FLIP-Tiny (0.51M parameters) reaches a mean IoU of 78.24% while SAM-H reaches 75.41% IoU (641.1M parameters). FLIP-Large even achieves 80.33% mean IoU (96.6M parameters), still running about 6$\times$ faster than SAM-H. We evaluate on six benchmarks in total. In five established benchmarks (Hypersim, KITTI-360, OpenImages, COCO, LVIS) FLIP consistently outperforms SAM and various variants of it. In our novel ObjaScale dataset, which stress-tests scale invariance with objects ranging from 0.0001% up-to 25% of the image area, we show that FLIP segments even very small objects accurately, where existing models fail severely. FLIP opens new possibilities for real-time, object-centric vision applications and offers much higher energy efficiency. We believe that FLIP can act as a powerful foundation model, as it is very well-suited to track objects over time, for example, when being integrated into slot-based scene segmentation architectures.