36 citations · 69 across the 7 of their papers we have counts for
7 papers
Zero-shot audio captioning with audio-language model guidance and audio context keywords
Leonard Salewski, Stefan Fauth, A. Sophia Koepke +1
Zero-shot audio captioning aims at automatically generating descriptive textual captions for audio content without prior training for this task. Different from speech recognition w…
Zero-shot Translation of Attention Patterns in VQA Models to Natural Language
Leonard Salewski, A. Sophia Koepke, Hendrik P. A. Lensch +1
Converting a model's internals to text can yield human-understandable insights about the model. Inspired by the recent success of training-free approaches for image captioning, we…
In-Context Impersonation Reveals Large Language Models' Strengths and Biases
Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto +2
In everyday conversations, humans can take on different roles and adapt their vocabulary to their chosen roles. We explore whether LLMs can take on, that is impersonate, different…
Diverse Video Captioning by Adaptive Spatio-temporal Attention
Zohreh Ghaderi, Leonard Salewski, Hendrik P. A. Lensch
To generate proper captions for videos, the inference needs to identify relevant concepts and pay attention to the spatial relationships between them as well as to the temporal dev…
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
Leonard Salewski, A. Sophia Koepke, Hendrik P. A. Lensch +1
Providing explanations in the context of Visual Question Answering (VQA) presents a fundamental problem in machine learning. To obtain detailed insights into the process of generat…
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
Maxime Kayser, Oana-Maria Camburu, Leonard Salewski +4
Recently, there has been an increasing number of efforts to introduce models capable of generating natural language explanations (NLEs) for their predictions on vision-language (VL…
Relational Generalized Few-Shot Learning
Xiahan Shi, Leonard Salewski, Martin Schiegg +2
Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available. Although this few-shot learning setup has receive…