1 code implementation • 15 Apr 2024 • Amir Erfan Eshratifar, Joao V. B. Soares, Kapil Thadani, Shaunak Mishra, Mikhail Kuznetsov, Yueh-Ning Ku, Paloma de Juan
Generating background scenes for salient objects plays a crucial role across various domains including creative design and e-commerce, as it enhances the presentation and context of subjects by integrating them into tailored environments.
no code implementations • 27 Jan 2021 • Manisha Verma, Kapil Thadani, Shaunak Mishra
In this work, we demonstrate the effectiveness of different attention based neural models that can directly exploit side information available in technical documents or verified forums (e. g., research publications on COVID-19 or WHO website).
no code implementations • COLING 2020 • Aakriti Gupta, Kapil Thadani, Neil O{'}Hare
In this work, we use the ARSC dataset to study a simple application of transfer learning approaches to few-shot classification.
no code implementations • 17 Aug 2020 • Shaunak Mishra, Manisha Verma, Yichao Zhou, Kapil Thadani, Wei Wang
Since major ad platforms typically run A/B tests for multiple advertisers in parallel, we explore the possibility of collaboratively learning ad creative refinement via A/B tests of multiple advertisers.
no code implementations • WS 2019 • Nasser Zalmout, Kapil Thadani, Aasish Pappu
This paper presents an approach for detecting and normalizing neologisms in social media content.
no code implementations • LREC 2016 • Yashar Mehdad, Am Stent, a, Kapil Thadani, Dragomir Radev, Youssef Billawala, Karolina Buchner
In this paper we report a comparison of various techniques for single-document extractive summarization under strict length budgets, which is a common commercial use case (e. g. summarization of news articles by news aggregators).