2 citations · 5 across the 27 of their papers we have counts for
28 papers
Implicit Causal Graph Construction in Text via Chain Discovery
Liesbeth Allein, Marie-Francine Moens
Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by treating each described cause-…
NewsRECON: News Article Retrieval for Image Contextualization
Jonathan Tonglet, Iryna Gurevych, Tinne Tuytelaars +1
Identifying when and where a news image was taken is crucial for journalists and forensic experts to produce credible stories and debunk misinformation. While many existing methods…
Reduction of Supervision for Biomedical Knowledge Discovery
Christos Theodoropoulos, Andrei Catalin Coman, James Henderson +1
Knowledge discovery is hindered by the increasing volume of publications and the scarcity of extensive annotated data. To tackle the challenge of information overload, it is essent…
Towards More Accurate Personalized Image Generation: Addressing Overfitting and Evaluation Bias
Mingxiao Li, Tingyu Qu, Tinne Tuytelaars +1
Personalized image generation via text prompts has great potential to improve daily life and professional work by facilitating the creation of customized visual content. The aim of…
Protecting multimodal large language models against misleading visualizations
Jonathan Tonglet, Tinne Tuytelaars, Marie-Francine Moens +1
Visualizations play a pivotal role in daily communication in an increasingly data-driven world. Research on multimodal large language models (MLLMs) for automated chart understandi…
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…
Tuning In to Neural Encoding: Linking Human Brain and Artificial Supervised Representations of Language
Jingyuan Sun, Xiaohan Zhang, Marie-Francine Moens
To understand the algorithm that supports the human brain's language representation, previous research has attempted to predict neural responses to linguistic stimuli using embeddi…
Fine-tuned vs. Prompt-tuned Supervised Representations: Which Better Account for Brain Language Representations?
Jingyuan Sun, Marie-Francine Moens
To decipher the algorithm underlying the human brain's language representation, previous work probed brain responses to language input with pre-trained artificial neural network (A…
Sequence-to-Sequence Spanish Pre-trained Language Models
Vladimir Araujo, Maria Mihaela Trusca, Rodrigo Tufiño +1
In recent years, significant advancements in pre-trained language models have driven the creation of numerous non-English language variants, with a particular emphasis on encoder-o…
GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction
Andrei C. Coman, Christos Theodoropoulos, Marie-Francine Moens +1
Document-level relation extraction typically relies on text-based encoders and hand-coded pooling heuristics to aggregate information learned by the encoder. In this paper, we leve…
Beyond Document Page Classification: Design, Datasets, and Challenges
Jordy Van Landeghem, Sanket Biswas, Matthew B. Blaschko +1
This paper highlights the need to bring document classification benchmarking closer to real-world applications, both in the nature of data tested (: multi-channel, multi-paged,…
Visually-Aware Context Modeling for News Image Captioning
Tingyu Qu, Tinne Tuytelaars, Marie-Francine Moens
News Image Captioning aims to create captions from news articles and images, emphasizing the connection between textual context and visual elements. Recognizing the significance of…
Multimodal Distillation for Egocentric Action Recognition
Gorjan Radevski, Dusan Grujicic, Marie-Francine Moens +2
The focal point of egocentric video understanding is modelling hand-object interactions. Standard models, e.g. CNNs or Vision Transformers, which receive RGB frames as input perfor…