most citedInterpretable Oracle Bone Script Decipherment through Radical and Pictographic Analysis with LVLMs

1 citations · 1 across the 7 of their papers we have counts for

collaborators

12 papers

cs.CV2025

CME-CAD: Heterogeneous Collaborative Multi-Expert Reinforcement Learning for CAD Code Generation

Ke Niu, Haiyang Yu, Zhuofan Chen +7

Computer-Aided Design (CAD) is essential in industrial design, but the complexity of traditional CAD modeling and workflows presents significant challenges for automating the gener…

cs.CV2025

OmniPT: Unleashing the Potential of Large Vision Language Models for Pedestrian Tracking and Understanding

Teng Fu, Mengyang Zhao, Ke Niu +2

LVLMs have been shown to perform excellently in image-level tasks such as VQA and caption. However, in many instance-level tasks, such as visual grounding and object detection, LVL…

cs.LG2025

From Intent to Execution: Multimodal Chain-of-Thought Reinforcement Learning for Precise CAD Code Generation

Ke Niu, Haiyang Yu, Zhuofan Chen +4

Computer-Aided Design (CAD) plays a vital role in engineering and manufacturing, yet current CAD workflows require extensive domain expertise and manual modeling effort. Recent adv…

cs.CV20251 cited

Interpretable Oracle Bone Script Decipherment through Radical and Pictographic Analysis with LVLMs

Kaixin Peng, Mengyang Zhao, Haiyang Yu +2

As the oldest mature writing system, Oracle Bone Script (OBS) has long posed significant challenges for archaeological decipherment due to its rarity, abstractness, and pictographi…

cs.CV2025

IADGPT: Unified LVLM for Few-Shot Industrial Anomaly Detection, Localization, and Reasoning via In-Context Learning

Mengyang Zhao, Teng Fu, Haiyang Yu +2

Few-Shot Industrial Anomaly Detection (FS-IAD) has important applications in automating industrial quality inspection. Recently, some FS-IAD methods based on Large Vision-Language…

cs.CV2025

Beyond Task-Specific Reasoning: A Unified Conditional Generative Framework for Abstract Visual Reasoning

Fan Shi, Bin Li, Xiangyang Xue

Abstract visual reasoning (AVR) enables humans to quickly discover and generalize abstract rules to new scenarios. Designing intelligent systems with human-like AVR abilities has b…