Region-based Cluster Discrimination for Visual Representation Learning
Authors: name: Yin Xie; name: Kaicheng Yang; name: Xiang An; name: Kun Wu; name: Yongle Zhao; name: Weimo Deng; name: Zimin Ran; name: Yumeng Wang; name: Ziyong Feng; name: Roy Miles; name: Ismail Elezi; name: Jiankang Deng
Published: 2025-07-26 17:47:09

Abstract: Learning visual representations is foundational for a broad spectrum of
downstream tasks. Although recent vision-language contrastive models, such as
CLIP and SigLIP, have achieved impressive zero-shot performance via large-scale
vision-language alignment, their reliance on global representations constrains
their effectiveness for dense prediction tasks, such as grounding, OCR, and
segmentation. To address this gap, we introduce Region-Aware Cluster
Discrimination (RICE), a novel method that enhances region-level visual and OCR
capabilities. We first construct a billion-scale candidate region dataset and
propose a Region Transformer layer to extract rich regional semantics. We
further design a unified region cluster discrimination loss that jointly
supports object and OCR learning within a single classification framework,
enabling efficient and scalable distributed training on large-scale data.
Extensive experiments show that RICE consistently outperforms previous methods
on tasks, including segmentation, dense detection, and visual perception for
Multimodal Large Language Models (MLLMs). The pre-trained models have been
released at https://github.com/deepglint/MVT.