e-vil: A dataset and benchmark for natural language explanations in vision-language tasks M Kayser, OM Camburu, L Salewski, C Emde, V Do, Z Akata, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 82 | 2021 |
In-Context Impersonation Reveals Large Language Models' Strengths and Biases L Salewski, S Alaniz, I Rio-Torto, E Schulz, Z Akata Advances in Neural Information Processing Systems 36, 2024 | 62 | 2024 |
Clevr-x: A visual reasoning dataset for natural language explanations L Salewski, AS Koepke, HPA Lensch, Z Akata xxAI-Beyond Explainable AI: International Workshop, Held in Conjunction with …, 2022 | 25 | 2022 |
Relational generalized few-shot learning X Shi, L Salewski, M Schiegg, Z Akata, M Welling arXiv preprint arXiv:1907.09557, 2019 | 25 | 2019 |
Zero-shot audio captioning with audio-language model guidance and audio context keywords L Salewski, S Fauth, AS Koepke, Z Akata | 3 | 2023 |
Zero-shot Translation of Attention Patterns in VQA Models to Natural Language L Salewski, AS Koepke, HPA Lensch, Z Akata DAGM German Conference on Pattern Recognition, 2023 | 1 | 2023 |
Diverse Video Captioning by Adaptive Spatio-temporal Attention Z Ghaderi, L Salewski, HPA Lensch DAGM German Conference on Pattern Recognition, 409-425, 2022 | 1 | 2022 |
Adapting a base classifier to novel classes X Shi, M Schiegg, L Salewski, M Welling, Z Akata US Patent 11,481,649, 2022 | | 2022 |
Supplementary Materials: In-Context Impersonation Reveals Large Language Models’ Strengths and Biases L Salewski, S Alaniz, I Rio-Torto, E Schulz, Z Akata | | |