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Pradeep Natarajan

11 accepted papers

2024

HVCLIP: High-dimensional Vector in CLIP for Unsupervised Domain Adaptation

ECCV 2024poster

"Recent advancement in the large-scale image-text pre-training model (such as CLIP) has significantly improved unsupervised domain adaptation (UDA) by leveraging the pre-trained knowledge to bridge the source and target domain gap. However, Catastrophic forgetting still remains to be the main challe…

Cited by 2SourcePDFScholar
2023

Adversarial Robustness for Large Language NER models using Disentanglement and Word Attributions

EMNLP 2023long findings

Large language models (LLM's) have been widely used for several applications such as question answering, text classification and clustering. While the preliminary results across the aforementioned tasks looks promising, recent work has dived deep into LLM's performing poorly for complex Named Entity…

Cited by 0SourceScholar
2023

FashionNTM: Multi-turn Fashion Image Retrieval via Cascaded Memory

ICCV 2023poster

Multi-turn textual feedback-based fashion image retrieval focuses on a real-world setting, where users can iteratively provide information to refine retrieval results until they find an item that fits all their requirements. In this work, we present a novel memory-based method, called FashionNTM, fo…

Cited by 9PDFcodeScholar
2023

User-Controllable Arbitrary Style Transfer via Entropy Regularization

AAAI 2023technical

Ensuring the overall end-user experience is a challenging task in arbitrary style transfer (AST) due to the subjective nature of style transfer quality. A good practice is to provide users many instead of one AST result. However, existing approaches require to run multiple AST models or inference a…

2022

Asd-Transformer: Efficient Active Speaker Detection Using Self And Multimodal Transformers

ICASSP 2022accepted

Multimodal active speaker detection (ASD) methods assign a speaking/not-speaking label per individual in a video clip. ASD is critical for applications such as natural human-computer interaction, speaker diarization, and video reframing. Recent work has shown the success of transformers in multimoda…

Cited by 0SourceScholar
2022

CGF: Constrained Generation Framework for Query Rewriting in Conversational AI

EMNLP 2022industry

In conversational AI agents, Query Rewriting (QR) plays a crucial role in reducing user frictions and satisfying their daily demands. User frictions are caused by various reasons, such as errors in the conversational AI system, users’ accent or their abridged language. In this work, we present a nov…

2022

FPI: Failure Point Isolation in Large-scale Conversational Assistants

NAACL 2022industry

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. An error in one of these modules can cascade through the system. Given the…

Cited by 10SourcePDFScholar
2022

FashionVLP: Vision Language Transformer for Fashion Retrieval With Feedback

CVPR 2022poster

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. We propose a new vision-language transformer based model, FashionVLP, tha…

Cited by 120PDFScholar
2022

Improving Large-Scale Conversational Assistants using Model Interpretation based Training Sample Selection

EMNLP 2022industry

This paper presents an approach to identify samples from live traffic where the customer implicitly communicated satisfaction with Alexa’s responses, by leveraging interpretations of model behavior. Such customer signals are noisy and adding a large number of samples from live traffic to training se…

Cited by 2SourcePDFScholar
2021

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

EMNLP 2021main

The task of learning from only a few examples (called a few-shot setting) is of key importance and relevance to a real-world setting. For question answering (QA), the current state-of-the-art pre-trained models typically need fine-tuning on tens of thousands of examples to obtain good results. Their…

Cited by 52SourcePDFScholar
2021

Style-Aware Normalized Loss for Improving Arbitrary Style Transfer

CVPR 2021poster

Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST). Although appealing results have been widely reported in literature, our empirical studies on four well-known AST approaches (GoogleMagenta, AdaIN, LinearTransfer,…

Cited by 51PDFcodeScholar