1 citations · 2 across the 6 of their papers we have counts for
6 papers
HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities
Esra Dönmez, Pascal Tilli, Hsiu-Yu Yang +2
Image-Text-Matching (ITM) is one of the defacto methods of learning generalized representations from a large corpus in Vision and Language (VL). However, due to the weak associatio…
Discrete Subgraph Sampling for Interpretable Graph based Visual Question Answering
Pascal Tilli, Ngoc Thang Vu
Explainable artificial intelligence (XAI) aims to make machine learning models more transparent. While many approaches focus on generating explanations post-hoc, interpretable appr…
Explaining Caption-Image Interactions in CLIP Models with Second-Order Attributions
Lucas Möller, Pascal Tilli, Ngoc Thang Vu +1
Dual encoder architectures like Clip models map two types of inputs into a shared embedding space and predict similarities between them. Despite their wide application, it is, howe…
Prompting-based Synthetic Data Generation for Few-Shot Question Answering
Maximilian Schmidt, Andrea Bartezzaghi, Ngoc Thang Vu
Although language models (LMs) have boosted the performance of Question Answering, they still need plenty of data. Data annotation, in contrast, is a time-consuming process. This e…
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering
Pascal Tilli, Ngoc Thang Vu
The large success of deep learning based methods in Visual Question Answering (VQA) has concurrently increased the demand for explainable methods. Most methods in Explainable Artif…
Combining Data Generation and Active Learning for Low-Resource Question Answering
Maximilian Kimmich, Andrea Bartezzaghi, Jasmina Bogojeska +2
Neural approaches have become very popular in Question Answering (QA), however, they require a large amount of annotated data. In this work, we propose a novel approach that combin…