Co-LoRA: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients
Minhyuk Seo · Taeheon Kim · Hankook Lee · Jonghyun Choi · Tinne Tuytelaars
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Minhyuk Seo · Taeheon Kim · Hankook Lee · Jonghyun Choi · Tinne Tuytelaars
Baptiste Debes · Tinne Tuytelaars
Minjae Lee · Minhyuk Seo · Tingyu Qu · Tinne Tuytelaars · Jonghyun Choi
In continual instruction tuning (CIT) scenarios, where new instruction tuning data continuously arrive in an online streaming manner, training delays from large-scale data significantly hinder real-time adaptation. Data selection can mitigate this overhead, but existing strategies often rely on p...
Jonathan Tonglet · Tinne Tuytelaars · Marie-Francine Moens · Iryna Gurevych
Visualizations play a pivotal role in daily communication in an increasingly data-driven world. Research on multimodal large language models (MLLMs) for automated chart understanding has accelerated massively, with steady improvements on standard benchmarks. However, for MLLMs to be reliable, the...
Jonathan Tonglet · Jan Zimny · Tinne Tuytelaars · Iryna Gurevych
Misleading visualizations are a potent driver of misinformation on social media and the web. By violating chart design principles, they distort data and lead readers to draw inaccurate conclusions. Prior work has shown that both humans and multimodal large language models (MLLMs) are frequently d...