Search Results for author: Ankita Pasad

Found 6 papers, 1 papers with code

On the Use of External Data for Spoken Named Entity Recognition

no code implementations14 Dec 2021 Ankita Pasad, Felix Wu, Suwon Shon, Karen Livescu, Kyu J. Han

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?

Knowledge Distillation Named Entity Recognition +4

Layer-wise Analysis of a Self-supervised Speech Representation Model

no code implementations10 Jul 2021 Ankita Pasad, Ju-chieh Chou, Karen Livescu

Recently proposed self-supervised learning approaches have been successful for pre-training speech representation models.

Automatic Speech Recognition Self-Supervised Learning

Taskology: Utilizing Task Relations at Scale

no code implementations CVPR 2021 Yao Lu, Sören Pirk, Jan Dlabal, Anthony Brohan, Ankita Pasad, Zhao Chen, Vincent Casser, Anelia Angelova, Ariel Gordon

Many computer vision tasks address the problem of scene understanding and are naturally interrelated e. g. object classification, detection, scene segmentation, depth estimation, etc.

Depth Estimation Motion Estimation +3

Improving Semantic Segmentation through Spatio-Temporal Consistency Learned from Videos

no code implementations11 Apr 2020 Ankita Pasad, Ariel Gordon, Tsung-Yi Lin, Anelia Angelova

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.

Semantic Segmentation

On the Contributions of Visual and Textual Supervision in Low-Resource Semantic Speech Retrieval

no code implementations24 Apr 2019 Ankita Pasad, Bowen Shi, Herman Kamper, Karen Livescu

Recent work has shown that speech paired with images can be used to learn semantically meaningful speech representations even without any textual supervision.

Visual Grounding

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