Search Results for author: Sandra Sajeev

Found 7 papers, 2 papers with code

Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection

1 code implementation24 Jul 2023 Christopher Clarke, Matthew Hall, Gaurav Mittal, Ye Yu, Sandra Sajeev, Jason Mars, Mei Chen

In this paper, we present Rule By Example (RBE): a novel exemplar-based contrastive learning approach for learning from logical rules for the task of textual content moderation.

Contrastive Learning Hate Speech Detection

Rethinking Multimodal Content Moderation from an Asymmetric Angle with Mixed-modality

no code implementations17 May 2023 Jialin Yuan, Ye Yu, Gaurav Mittal, Matthew Hall, Sandra Sajeev, Mei Chen

There is a rapidly growing need for multimodal content moderation (CM) as more and more content on social media is multimodal in nature.

PivoTAL: Prior-Driven Supervision for Weakly-Supervised Temporal Action Localization

no code implementations CVPR 2023 Mamshad Nayeem Rizve, Gaurav Mittal, Ye Yu, Matthew Hall, Sandra Sajeev, Mubarak Shah, Mei Chen

To address this, we present PivoTAL, Prior-driven Supervision for Weakly-supervised Temporal Action Localization, to approach WTAL from a localization-by-localization perspective by learning to localize the action snippets directly.

Weakly Supervised Action Localization Weakly Supervised Temporal Action Localization

ProTeGe: Untrimmed Pretraining for Video Temporal Grounding by Video Temporal Grounding

no code implementations CVPR 2023 Lan Wang, Gaurav Mittal, Sandra Sajeev, Ye Yu, Matthew Hall, Vishnu Naresh Boddeti, Mei Chen

We present ProTeGe as the first method to perform VTG-based untrimmed pretraining to bridge the gap between trimmed pretrained backbones and downstream VTG tasks.

text similarity

Contextual Bandit Applications in Customer Support Bot

no code implementations6 Dec 2021 Sandra Sajeev, Jade Huang, Nikos Karampatziakis, Matthew Hall, Sebastian Kochman, Weizhu Chen

We do, however, have access to partial feedback provided by the user (clicks, surveys, and other events) which can be leveraged to improve the user experience.

Multi-Armed Bandits

xBD: A Dataset for Assessing Building Damage from Satellite Imagery

4 code implementations21 Nov 2019 Ritwik Gupta, Richard Hosfelt, Sandra Sajeev, Nirav Patel, Bryce Goodman, Jigar Doshi, Eric Heim, Howie Choset, Matthew Gaston

xBD is the largest building damage assessment dataset to date, containing 850, 736 building annotations across 45, 362 km\textsuperscript{2} of imagery.

2D Semantic Segmentation Change Detection +2

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