Shuaixing Chen

Hi! I am an undergraduate at Electrical Engineering (EE),
School of Electronical Engineering and Electrical Engineering (SEIEE),
Shanghai Jiao Tong University (SJTU).

Currently under guide of Prof. Dequan Wang, research on the field of LLM and Nerf. In the coming summer, I will go ro Cornell to do Summer Research under guide of Prof. Tapo!
In the past, I have participated in projects related to vision, including active target tracking and target detection in autonomous driving. For more details, please refer to the research section. These experiences have been greatly beneficial to me.
I am interested in the field of LLM Agents and Robots.

Recently, I have a paper to be published!
CV
Contact: alkdischen@foxmail.com

Selected Research

• LLM Agents Consensus for AD Simulation | RA        Oct 2023 - Feb 2024

  • Advisor: Prof. Dequan Wang

  • Integrated the Raft algorithm with LLMs to achieve consensus among LLM Agents in AD simulation.

  • Proposed a new method combining Raft and LLM has been proposed, dynamically grouping agents to achieve information synchronization, ultimately ensuring communication among LLMs and environmental stability during the simLLM simulation process.

  • Constructed an AD simulation env that synchronizes and merges content among LLM agents, proving Raft's importance. The first-author paper has been submitted to ICIR 2024 Workshop AGI.

• Image Generation and Editing System based on Diffusion Models |RA        May 2023 - Present

  • Advisor: Prof. Yichao Yan

  • Proposed a method combining Gaussian splatting and morphing field reconstruction to achieve video obstacle and shadow removal.

  • Integrates the concepts of dynamic scene reconstruction and HyperNeRF, taking into account the capabilities of Gaussian splatting, suitable for more powerful video reconstruction and shadow obstacle removal work.

  • Boosted the PSNR by 3-4% compared to traditional methods. The relevant paper is being prepared for submission to ECCV 2024

• Small Object Detection and Recognition in Autonomous Driving | RA        March 2023 - Feb 2024

  • Advisor: Prof. Manhua Liu

  • Proposed adjustments to the DETR network for small object detection tasks and explored the effectiveness of mask networks in detecting small objects.

  • Combined and adjusted DINO, DETR, and other end-to-end object detection networks with mask self-supervised networks for enhanced small object detection, exploring mask network capabilities in this context.

  • Boosted DETR by 3 to 5 AP points, enhancing small object detection and proving mask networks’ effectiveness for such tasks.