REN: Fast and Efficient Region Encodings from Patch-Based Image Encoders

2025-05-23

AI Summary

Existing methods rely on category-agnostic segmenters (e.g., SAM) jointly with patch encoders (e.g., DINO) to generate region representations, but explicit segmentation incurs substantial computational overhead. This paper proposes the Region Encoder Network (REN), a lightweight cross-attention module that directly consumes point prompts and patch features to produce semantically aligned region tokens in an end-to-end manner—eliminating explicit segmentation entirely. REN is plug-and-play compatible with diverse patch encoders, including DINO, DINOv2, and OpenCLIP. On Ego4D VQ2D, it achieves state-of-the-art performance; on Visual Haystacks—spanning single-needle localization, semantic segmentation, and retrieval—it surpasses both SAM and the underlying encoder baselines. Crucially, REN delivers a 60× inference speedup and 35× memory reduction while preserving high-quality, compact, and efficient region representations.

Technology Category

• Computer Vision: Segmentation
• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

We introduce the Region Encoder Network (REN), a fast and effective model for generating region-based image representations using point prompts. Recent methods combine class-agnostic segmenters (e.g., SAM) with patch-based image encoders (e.g., DINO) to produce compact and effective region representations, but they suffer from high computational cost due to the segmentation step. REN bypasses this bottleneck using a lightweight module that directly generates region tokens, enabling 60x faster token generation with 35x less memory, while also improving token quality. It uses a few cross-attention blocks that take point prompts as queries and features from a patch-based image encoder as keys and values to produce region tokens that correspond to the prompted objects. We train REN with three popular encoders-DINO, DINOv2, and OpenCLIP-and show that it can be extended to other encoders without dedicated training. We evaluate REN on semantic segmentation and retrieval tasks, where it consistently outperforms the original encoders in both performance and compactness, and matches or exceeds SAM-based region methods while being significantly faster. Notably, REN achieves state-of-the-art results on the challenging Ego4D VQ2D benchmark and outperforms proprietary LMMs on Visual Haystacks' single-needle challenge. Code and models are available at: https://github.com/savya08/REN.

Problem

• Fast region-based image representations using point prompts
• Reducing computational cost of segmentation-based methods
• Improving token quality and efficiency in region encoding

Innovation

• Lightweight module for fast region token generation
• Cross-attention blocks with point prompts as queries
• Compatible with multiple patch-based image encoders