Search Results for author: Stephen Rawls

Found 8 papers, 2 papers with code

Translation-Enhanced Multilingual Text-to-Image Generation

no code implementations30 May 2023 Yaoyiran Li, Ching-Yun Chang, Stephen Rawls, Ivan Vulić, Anna Korhonen

Research on text-to-image generation (TTI) still predominantly focuses on the English language due to the lack of annotated image-caption data in other languages; in the long run, this might widen inequitable access to TTI technology.

Cross-lingual Text-to-Image Generation Crosslingual Text-to-Image Generation +5

Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data

no code implementations4 Apr 2023 Vladislav Lialin, Stephen Rawls, David Chan, Shalini Ghosh, Anna Rumshisky, Wael Hamza

Currently popular video-text data mining approach via automatic speech recognition (ASR) used in HowTo100M provides low-quality captions that often do not refer to the video content.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

1 code implementation2 Aug 2022 Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, Rahul Gupta, Wael Hamza, Haidar Khan, Charith Peris, Stephen Rawls, Andy Rosenbaum, Anna Rumshisky, Chandana Satya Prakash, Mukund Sridhar, Fabian Triefenbach, Apurv Verma, Gokhan Tur, Prem Natarajan

In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks.

Causal Language Modeling Common Sense Reasoning +8

Implicit Language Model in LSTM for OCR

1 code implementation23 May 2018 Ekraam Sabir, Stephen Rawls, Prem Natarajan

Neural networks have become the technique of choice for OCR, but many aspects of how and why they deliver superior performance are still unknown.

Language Modelling Optical Character Recognition (OCR)

Pose-Aware Face Recognition in the Wild

no code implementations CVPR 2016 Iacopo Masi, Stephen Rawls, Gerard Medioni, Prem Natarajan

We propose a method to push the frontiers of unconstrained face recognition in the wild, focusing on the problem of extreme pose variations.

Face Recognition

Learning Document Image Binarization from Data

no code implementations4 May 2015 Yue Wu, Stephen Rawls, Wael Abd-Almageed, Premkumar Natarajan

In this paper we present a fully trainable binarization solution for degraded document images.


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