Search Results for author:
Found 6 papers, 1 papers with code
On the Use of External Data for Spoken Named Entity Recognition
In this work we focus on low-resource spoken named entity recognition (NER) and address the question: Beyond self-supervised pre-training, how can we use external speech and/or text data that are not annotated for the task?
SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech
Historically these have focused on automatic speech recognition (ASR), speaker identification, or other lower-level tasks.
Ranked #1 on
Named Entity Recognition
on SLUE
Layer-wise Analysis of a Self-supervised Speech Representation Model
Recently proposed self-supervised learning approaches have been successful for pre-training speech representation models.
Taskology: Utilizing Task Relations at Scale
Many computer vision tasks address the problem of scene understanding and are naturally interrelated e. g. object classification, detection, scene segmentation, depth estimation, etc.
Improving Semantic Segmentation through Spatio-Temporal Consistency Learned from Videos
We leverage unsupervised learning of depth, egomotion, and camera intrinsics to improve the performance of single-image semantic segmentation, by enforcing 3D-geometric and temporal consistency of segmentation masks across video frames.
On the Contributions of Visual and Textual Supervision in Low-Resource Semantic Speech Retrieval
Recent work has shown that speech paired with images can be used to learn semantically meaningful speech representations even without any textual supervision.
