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LJ

Lianwen Jin

AAAI 2026×4ICLR 2026×3ACL 2026×2CVPR 2026多模态推理检索与知识对齐与偏好可解释性
新星分资深
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0 一作 · 10 篇 · AAAI 2026 / ICLR 2026 / ACL 2026 / CVPR 2026

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2026 顶会论文

10
作者位次 #4/4ICLR 2026Poster· Computer Vision / Vision Models & Multimodal

Omni-IML: Towards Unified Interpretable Image Manipulation Localization

Chenfan Qu · Yiwu Zhong · Fengjun Guo · Lianwen Jin

多模态可解释性OpenReview
作者位次 #5/5ICLR 2026Poster· Deep Learning / Generative Models and Autoencoders

DiffInk: Glyph- and Style-Aware Latent Diffusion Transformer for Text to Online Handwriting Generation

Wei Pan · Huiguo He · Hiuyi Cheng · Yilin Shi · Lianwen Jin

作者位次 #6/6ACL 2026Poster· Natural Language Processing

Draft, Verify, Restore: Self-Refining Historical Inscription Restoration with a Unified MLLM

Yuyi Zhang · Junle Liu · Peirong Zhang · Jianliang Liu · Zhenhua Yang · Lianwen Jin

摘要

Inscriptions are invaluable cultural heritage, yet centuries of degradation (e.g., fractures, erosion, oxidation) have rendered many partially illegible. Existing Historical Inscription Restoration (HIR) methods rely on task-separated pipelines with irreversible error accumulation and patch-based...

检索与知识推理对齐与偏好OpenReview 项目页
作者位次 #5/5ACL 2026Findings· Findings of ACL

H is D oc- OCR : Restoring Visual Grounding in MLLM s for C hinese Historical Document OCR

Jiahuan Cao · Yongxin Shi · Zeyu Shan · Zhengyang Lu · Lianwen Jin

摘要

Chinese historical documents encode millennia of cultural heritage, yet remain largely inaccessible to computational analysis. While multimodal large language models (MLLMs) have achieved strong performance on modern document OCR, their application to historical Chinese texts suffers from severe...

多模态对齐与偏好安全OpenReview 项目页

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