1 citations · 1 across the 9 of their papers we have counts for
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Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition
Shengming Yin, Zekai Zhang, Zecheng Tang +11
Recent visual generative models often struggle with consistency during image editing due to the entangled nature of raster images, where all visual content is fused into a single c…
ReCAD: Reinforcement Learning Enhanced Parametric CAD Model Generation with Vision-Language Models
Jiahao Li, Yusheng Luo, Yunzhong Lou +1
We present ReCAD, a reinforcement learning (RL) framework that bootstraps pretrained large models (PLMs) to generate precise parametric computer-aided design (CAD) models from mult…
Global Regulation and Excitation via Attention Tuning for Stereo Matching
Jiahao Li, Xinhong Chen, Zhengmin Jiang +3
Stereo matching achieves significant progress with iterative algorithms like RAFT-Stereo and IGEV-Stereo. However, these methods struggle in ill-posed regions with occlusions, text…
Gradient Surgery for Safe LLM Fine-Tuning
Biao Yi, Jiahao Li, Baolei Zhang +4
Fine-tuning-as-a-Service introduces a critical vulnerability where a few malicious examples mixed into the user's fine-tuning dataset can compromise the safety alignment of Large L…
CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generation
Jiahao Li, Weijian Ma, Xueyang Li +3
Recently, Large Language Models (LLMs) have achieved significant success, prompting increased interest in expanding their generative capabilities beyond general text into domain-sp…
Seek-CAD: A Self-refined Generative Modeling for 3D Parametric CAD Using Local Inference via DeepSeek
Xueyang Li, Jiahao Li, Yu Song +2
The advent of Computer-Aided Design (CAD) generative modeling will significantly transform the design of industrial products. The recent research endeavor has extended into the rea…
FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model
Jun Zhou, Jiahao Li, Zunnan Xu +6
Currently, instruction-based image editing methods have made significant progress by leveraging the powerful cross-modal understanding capabilities of vision language models (VLMs)…