2026
Zhang, Sicheng; Naseer, Muhammad Muzammal; Xie, Binzhu; Suryanto, Naufal; Qiu, Shi; Bentahar, Jamal; Akhtar, Naveed; Shah, Mubarak
ReasonCLIP-58M: Visually Grounded Commonsense Reasoning Supervision for CLIP Conference
European Conference on Computer Vision (ECCV), Springer, 2026.
Abstract | Tags: Commonsense Reasoning, ECCV, Vision-Language Models | Links:
@conference{zhang2026reasonclipb,
title = {ReasonCLIP-58M: Visually Grounded Commonsense Reasoning Supervision for CLIP},
author = {Sicheng Zhang and Muhammad Muzammal Naseer and Binzhu Xie and Naufal Suryanto and Shi Qiu and Jamal Bentahar and Naveed Akhtar and Mubarak Shah},
url = {https://arxiv.org/abs/2606.26794},
year = {2026},
date = {2026-09-07},
urldate = {2026-01-01},
booktitle = {European Conference on Computer Vision (ECCV)},
publisher = {Springer},
abstract = {CLIP and its variants are widely adopted visual backbones in multimodal systems, but their pretraining remains dominated by descriptive image-text alignment. As downstream applications increasingly demand visually grounded commonsense inference and compositional reasoning, it remains unclear whether CLIP-style encoders can support such reasoning without architectural changes. To address this, we present ReasonCLIP-58M, a continual pretraining framework that integrates large-scale reasoning supervision into CLIP-style models through our two-stage strategy, which progressively integrates reasoning signals while preserving descriptive alignment, followed by category-structured reasoning supervision. To support this framework, we construct two complementary datasets and a benchmark: ReasonLite-42M, with open-form, visually verifiable reasoning captions; ReasonPro-16M, with category-specific reasoning supervision; and RCLIP-Bench for diagnostic evaluation of visually grounded reasoning. We train a family of ReasonCLIP that improves visually grounded commonsense and compositional reasoning while also enhancing zero-shot retrieval performance. As a drop-in visual encoder for multimodal large language models such as LLaVA-NeXT, ReasonCLIP delivers consistent gains without additional inference cost, demonstrating that structured reasoning supervision enhances the expressive capacity of CLIP-style visual representations. All datasets, models, and training code are available at this https URL.},
keywords = {Commonsense Reasoning, ECCV, Vision-Language Models},
pubstate = {published},
tppubtype = {conference}
}
CLIP and its variants are widely adopted visual backbones in multimodal systems, but their pretraining remains dominated by descriptive image-text alignment. As downstream applications increasingly demand visually grounded commonsense inference and compositional reasoning, it remains unclear whether CLIP-style encoders can support such reasoning without architectural changes. To address this, we present ReasonCLIP-58M, a continual pretraining framework that integrates large-scale reasoning supervision into CLIP-style models through our two-stage strategy, which progressively integrates reasoning signals while preserving descriptive alignment, followed by category-structured reasoning supervision. To support this framework, we construct two complementary datasets and a benchmark: ReasonLite-42M, with open-form, visually verifiable reasoning captions; ReasonPro-16M, with category-specific reasoning supervision; and RCLIP-Bench for diagnostic evaluation of visually grounded reasoning. We train a family of ReasonCLIP that improves visually grounded commonsense and compositional reasoning while also enhancing zero-shot retrieval performance. As a drop-in visual encoder for multimodal large language models such as LLaVA-NeXT, ReasonCLIP delivers consistent gains without additional inference cost, demonstrating that structured reasoning supervision enhances the expressive capacity of CLIP-style visual representations. All datasets, models, and training code are available at this https URL.