We introduce a linguistically enhanced combination of pre-training methods for transformers. The pre-training objectives include POS-tagging, synset prediction based on semantic knowledge graphs, and parent prediction based on dependency parse trees. Our approach...
We present sustain.AI, an intelligent, context-aware recommender system that assists auditors and financial investors as well as the general public to efficiently analyze companies’ sustainability reports. The tool leverages an end-to-end trainable architecture that...
Obtaining strong reproducible foundation language-audio models require open datasets of sufficient scale and quality. To pre-train contrastive language-audio model we compose large-scale sound effects dataset with detailed text descriptions for each sample. Generating...
In the era of big data and artificial intelligence, distributed machine learning has emerged as a promising solution to address privacy and security concerns while fostering collaboration between multiple parties. However, with the data increased in terms of...
We present a method that simultaneously addresses the tasks of dynamic scene novel-view synthesis and six degree-of-freedom (6-DOF) tracking of all dense scene elements. We follow an analysis-by-synthesis framework, inspired by recent work that models scenes as a...
Accurately perceiving and tracking instances over time is essential for the decision-making processes of autonomous agents interacting safely in dynamic environments. With this intention, we propose Mask4Former for the challenging task of 4D panoptic segmentation of...
