Search Results for author: Pradeep Natarajan

Found 8 papers, 1 papers with code

FPI: Failure Point Isolation in Large-scale Conversational Assistants

no code implementations NAACL (ACL) 2022 Rinat Khaziev, Usman Shahid, Tobias Röding, Rakesh Chada, Emir Kapanci, Pradeep Natarajan

Large-scale conversational assistants such as Cortana, Alexa, Google Assistant and Siri process requests through a series of modules for wake word detection, speech recognition, language understanding and response generation.

Response Generation speech-recognition +1

FashionVLP: Vision Language Transformer for Fashion Retrieval With Feedback

no code implementations CVPR 2022 Sonam Goenka, Zhaoheng Zheng, Ayush Jaiswal, Rakesh Chada, Yue Wu, Varsha Hedau, Pradeep Natarajan

Fashion image retrieval based on a query pair of reference image and natural language feedback is a challenging task that requires models to assess fashion related information from visual and textual modalities simultaneously.

Image Retrieval Retrieval

FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models

no code implementations EMNLP 2021 Rakesh Chada, Pradeep Natarajan

On the multilingual TydiQA benchmark, our model outperforms the XLM-Roberta-large by an absolute margin of upto 40 F1 points and an average of 33 F1 points in a few-shot setting (<= 64 training examples).

Few-Shot Learning Question Answering

Style-Aware Normalized Loss for Improving Arbitrary Style Transfer

1 code implementation CVPR 2021 Jiaxin Cheng, Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Prem Natarajan

Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST).

Style Transfer

Class-agnostic Object Detection

no code implementations28 Nov 2020 Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Premkumar Natarajan

Finally, we propose (1) baseline methods and (2) a new adversarial learning framework for class-agnostic detection that forces the model to exclude class-specific information from features used for predictions.

Ranked #97 on Image Classification on ObjectNet (using extra training data)

Class-agnostic Object Detection Image Classification +3

Zero-shot Event Detection using Multi-modal Fusion of Weakly Supervised Concepts

no code implementations CVPR 2014 Shuang Wu, Sravanthi Bondugula, Florian Luisier, Xiaodan Zhuang, Pradeep Natarajan

Current state-of-the-art systems for visual content analysis require large training sets for each class of interest, and performance degrades rapidly with fewer examples.

Event Detection

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