23 citations · 30 across the 26 of their papers we have counts for
6 papers · 1 filter
How to Merge Your Multimodal Models Over Time?
Sebastian Dziadzio, Vishaal Udandarao, Karsten Roth +4
Model merging combines multiple expert models - finetuned from a base foundation model on diverse tasks and domains - into a single, more capable model. However, most existing mode…
ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities
Adhiraj Ghosh, Sebastian Dziadzio, Ameya Prabhu +3
Traditional fixed test sets fall short in evaluating open-ended capabilities of foundation models. To address this, we propose ONEBench(OpeN-Ended Benchmarking), a new testing para…
Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…
A Practitioner's Guide to Continual Multimodal Pretraining
Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio +7
Multimodal foundation models serve numerous applications at the intersection of vision and language. Still, despite being pretrained on extensive data, they become outdated over ti…
CiteME: Can Language Models Accurately Cite Scientific Claims?
Ori Press, Andreas Hochlehnert, Ameya Prabhu +3
Thousands of new scientific papers are published each month. Such information overload complicates researcher efforts to stay current with the state-of-the-art as well as to verify…
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies
Zhongrui Gui, Shuyang Sun, Runjia Li +5
Continual segmentation has not yet tackled the challenge of improving open-vocabulary segmentation models with training data for accurate segmentation across large, continually exp…