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

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.