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Sebastian Dziadzio

6 accepted papers

2026

ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models

Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma, Akash Gupta, Samuel Roberts, Ioana Croitoru +27
ICML 2026poster

Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children or animals. Despite this, they attain high scores on many popular visual benchmarks, with headroom rapidly eroded by surging model progress. To address …

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2025

How to Merge Your Multimodal Models Over Time?

Sebastian Dziadzio, Vishaal Udandarao, Karsten Roth, Ameya Prabhu, Zeynep Akata, Samuel Albanie +1
CVPR 2025poster

Model merging combines expert models---each finetuned from a shared foundation model on diverse tasks and domains---into a single, more capable base model. However, existing model merging approaches assume all experts to be available simultaneously. In reality, new tasks and domains emerge continuou…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2025

ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities

Adhiraj Ghosh, Sebastian Dziadzio, Ameya Prabhu, Vishaal Udandarao, Samuel Albanie, Matthias Bethge
ACL 2025long

Traditional fixed test datasets fall short in evaluating the open-ended capabilities of foundation models. To address this, we propose ONEBench (OpeN-Ended Benchmarking), a new paradigm that consolidates individual evaluation datasets into a unified, ever-expanding sample pool. ONEBench enables cust…

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2024

A Practitioner's Guide to Real-World Continual Multimodal Pretraining

Vishaal Udandarao, Karsten Roth, Sebastian Dziadzio, Ameya Prabhu, Mehdi Cherti, Oriol Vinyals +4
NeurIPS 2024poster

Multimodal foundation models serve numerous applications at the intersection of vision and language. Still, despite being pretrained on extensive data, they become outdated over time. To keep models updated, research into continual pretraining mainly explores scenarios with either (1) infrequent, in…

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2021

Fake It Till You Make It: Face Analysis in the Wild Using Synthetic Data Alone

Erroll Wood, Tadas Baltrušaitis, Charlie Hewitt, Sebastian Dziadzio, Thomas J. Cashman, Jamie Shotton
ICCV 2021poster

We demonstrate that it is possible to perform face-related computer vision in the wild using synthetic data alone. The community has long enjoyed the benefits of synthesizing training data with graphics, but the domain gap between real and synthetic data has remained a problem, especially for human…

Cited by 339PDF ↗code ↗Scholar ↗
2021

Full-Body Motion From a Single Head-Mounted Device: Generating SMPL Poses From Partial Observations

Andrea Dittadi, Sebastian Dziadzio, Darren Cosker, Ben Lundell, Thomas J. Cashman, Jamie Shotton
ICCV 2021poster

The increased availability and maturity of head-mounted and wearable devices opens up opportunities for remote communication and collaboration. However, the signal streams provided by these devices (e.g., head pose, hand pose, and gaze direction) do not represent a whole person. One of the main open…

Cited by 69PDF ↗Scholar ↗