210 citations · 356 across the 21 of their papers we have counts for
21 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…
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…
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…
PDiscoNet: Semantically consistent part discovery for fine-grained recognition
Robert van der Klis, Stephan Alaniz, Massimiliano Mancini +4
Fine-grained classification often requires recognizing specific object parts, such as beak shape and wing patterns for birds. Encouraging a fine-grained classification model to fir…