2 citations · 5 across the 27 of their papers we have counts for
12 papers · 1 filter
TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models
Tingyu Qu, Mingxiao Li, Tinne Tuytelaars +1
Recent advances in multimodal Large Language Models (LLMs) have shown great success in understanding multi-modal contents. For video understanding tasks, training-based video LLMs…
End-to-end Planner Training for Language Modeling
Nathan Cornille, Florian Mai, Jingyuan Sun +1
Through end-to-end training to predict the next token, LLMs have become valuable tools for various tasks. Enhancing their core training in language modeling can improve numerous do…
Learning to Plan Long-Term for Language Modeling
Florian Mai, Nathan Cornille, Marie-Francine Moens
Modern language models predict the next token in the sequence by considering the past text through a powerful function such as attention. However, language models have no explicit…
"Image, Tell me your story!" Predicting the original meta-context of visual misinformation
Jonathan Tonglet, Marie-Francine Moens, Iryna Gurevych
To assist human fact-checkers, researchers have developed automated approaches for visual misinformation detection. These methods assign veracity scores by identifying inconsistenc…
Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models
Vladimir Araujo, Marie-Francine Moens, Tinne Tuytelaars
Parameter-efficient fine-tuning (PEFT) methods are increasingly used with pre-trained language models (PLMs) for continual learning (CL). These methods typically involve training a…
Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors
Andrei C. Coman, Christos Theodoropoulos, Marie-Francine Moens +1
We propose Fast-and-Frugal Text-Graph (FnF-TG) Transformers, a Transformer-based framework that unifies textual and structural information for inductive link prediction in text-att…
Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer's Disease
Christos Theodoropoulos, Andrei Catalin Coman, James Henderson +1
The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework desi…
DM-Align: Leveraging the Power of Natural Language Instructions to Make Changes to Images
Maria Mihaela Trusca, Tinne Tuytelaars, Marie-Francine Moens
Text-based semantic image editing assumes the manipulation of an image using a natural language instruction. Although recent works are capable of generating creative and qualitativ…
Object-Attribute Binding in Text-to-Image Generation: Evaluation and Control
Maria Mihaela Trusca, Wolf Nuyts, Jonathan Thomm +4
Current diffusion models create photorealistic images given a text prompt as input but struggle to correctly bind attributes mentioned in the text to the right objects in the image…
Learning to Plan for Language Modeling from Unlabeled Data
Nathan Cornille, Marie-Francine Moens, Florian Mai
By training to predict the next token in an unlabeled corpus, large language models learn to perform many tasks without any labeled data. However, their next-token-prediction objec…
Animate Your Motion: Turning Still Images into Dynamic Videos
Mingxiao Li, Bo Wan, Marie-Francine Moens +1
In recent years, diffusion models have made remarkable strides in text-to-video generation, sparking a quest for enhanced control over video outputs to more accurately reflect user…
Introducing Routing Functions to Vision-Language Parameter-Efficient Fine-Tuning with Low-Rank Bottlenecks
Tingyu Qu, Tinne Tuytelaars, Marie-Francine Moens
Mainstream parameter-efficient fine-tuning (PEFT) methods, such as LoRA or Adapter, project a model's hidden states to a lower dimension, allowing pre-trained models to adapt to ne…