Boyong Wu 吴博镛

Hello hello! :) I’m a graduating M.Sc. Computer Science student at the Technical University of Munich. I work on multimodal learning, computer vision and explainable AI, and my favourite part is putting the research ideas into the pipeline that runs them.

I am a student researcher in the Explainable Machine Learning group at Helmholtz Munich, with Zeynep Akata, where I am having an amazing time. My first paper was recently accepted at ECCV 2026, where we analysed how multimodal large language models process visual information throughout their layers: why and how LLM layers progressively recover visual quality, and the influence of bidirectional attention on MLLMs. I’ve also shipped software in industry, CI/CD for the Volkswagen Group In-Car App Store at CARIAD, tried building a startup called Squareflick, and assembly line engineering at Audi.

Away from that I build things for the fun of it, rendering, diffusion, VR, and small web tools for problems that annoy me.

I also like badminton, snowboarding and great food :)

Github     G. Scholar     LinkedIn

b.wu at tum dot de

From Drop-off to Recovery poster

From Drop-off to Recovery: A Mechanistic Analysis of Segmentation in MLLMs

Boyong Wu, Sanghwan Kim, Zeynep Akata
European Conference on Computer Vision (ECCV) 2026
Springer  •   arXiv  •   Code (Coming Soon)

Siemens
2026 – Present

AI consultant in the AI and New Technologies team at Siemens AG.

Helmholtz Munich
2025 – 2026

Student researcher in the Explainable Machine Learning group at Helmholtz Munich, working with Sanghwan Kim and Zeynep Akata on multimodal learning, segmentation and explainable AI.

CARIAD
2024

Software engineer at CARIAD SE, on the In-Car App Store for the Volkswagen Group. Designed and implemented the CI/CD pipelines for the app store repository, and automated sprint governance and reporting pipelines using REST APIs.

Audi
2019 – 2020

Industrial engineering intern at Audi AG, on assembly for the A6 and A7. Devised a plan to reduce assembly time by 40% at one station by evaluating assembly steps and part variance.

Things I built to understand how they work, each one from scratch.

How does text to image diffusion work?

Sampling loops, classifier-free guidance, inpainting and visual anagrams on a pretrained model, then a denoising U-Net trained from scratch to show where the pictures actually come from.
Code

Tower Wizard

A hand-tracked VR game for the Meta Quest: stand on a tower, cast spells with your bare hands and hold off waves of monsters until the timer runs out. Built in Unity 6 with the Meta XR SDK.
Built with a team of four
Code

Morph my Face into Taylor Swift!

Hand-labelled correspondences, Delaunay triangulation and affine warps that walk one face into another, then average a population into a mean face and push past it into caricature.
Code

NeRF explained

Fitting a neural field to a single image first, then a full neural radiance field from multi-view photographs, with volume rendering and novel views.
Code

How does the panorama feature on our phones work?

Homographies and rectification by hand, then the automatic version: Harris corners, adaptive non-maximal suppression, feature descriptors, matching and RANSAC.
Code

This is how we morph 2 single photos into a one seamlessly

Finite difference and derivative-of-Gaussian filters, image sharpening, hybrid images, and multiresolution blending with Gaussian and Laplacian stacks.
Code

Small web tools, built because something was annoying me.

Squareflick

Use the Kanban principle with QR codes to keep track of your consumable materials.

Squareflick Time

Remember the classic hourly chime on Casio watches? You can have that again, in a browser tab.

restock.link

My private ChatGPT.