73 citations · 194 across the 74 of their papers we have counts for
11 papers · 1 filter
How should the advent of large language models affect the practice of science?
Marcel Binz, Stephan Alaniz, Adina Roskies +15
Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the…
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…
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
Luca Eyring, Dominik Klein, Théo Uscidda +4
In optimal transport (OT), a Monge map is known as a mapping that transports a source distribution to a target distribution in the most cost-efficient way. Recently, multiple neura…
Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships
Abhra Chaudhuri, Massimiliano Mancini, Zeynep Akata +1
Recent advances in fine-grained representation learning leverage local-to-global (emergent) relationships for achieving state-of-the-art results. The relational representations rel…
Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model
Karsten Roth, Lukas Thede, Almut Sophia Koepke +3
Training deep networks requires various design decisions regarding for instance their architecture, data augmentation, or optimization. In this work, we find these training variati…
Vision-by-Language for Training-Free Compositional Image Retrieval
Shyamgopal Karthik, Karsten Roth, Massimiliano Mancini +1
Given an image and a target modification (e.g an image of the Eiffel tower and the text "without people and at night-time"), Compositional Image Retrieval (CIR) aims to retrieve th…
Video-adverb retrieval with compositional adverb-action embeddings
Thomas Hummel, Otniel-Bogdan Mercea, A. Sophia Koepke +1
Retrieving adverbs that describe an action in a video poses a crucial step towards fine-grained video understanding. We propose a framework for video-to-adverb retrieval (and vice…
Text-to-feature diffusion for audio-visual few-shot learning
Otniel-Bogdan Mercea, Thomas Hummel, A. Sophia Koepke +1
Training deep learning models for video classification from audio-visual data commonly requires immense amounts of labeled training data collected via a costly process. A challengi…
Inducing anxiety in large language models can induce bias
Julian Coda-Forno, Kristin Witte, Akshay K. Jagadish +3
Large language models (LLMs) are transforming research on machine learning while galvanizing public debates. Understanding not only when these models work well and succeed but also…
Machine Psychology
Thilo Hagendorff, Ishita Dasgupta, Marcel Binz +5
Large language models (LLMs) show increasingly advanced emergent capabilities and are being incorporated across various societal domains. Understanding their behavior and reasoning…