Search Results for author: Ayush Jaiswal

Found 17 papers, 4 papers with code

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

Style-Aware Normalized Loss for Improving Arbitrary Style Transfer

no code implementations 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.

Class-agnostic Object Detection object-detection +1

MEG: Multi-Evidence GNN for Multimodal Semantic Forensics

no code implementations23 Nov 2020 Ekraam Sabir, Ayush Jaiswal, Wael AbdAlmageed, Prem Natarajan

The problem setup requires algorithms to perform multimodal semantic forensics to authenticate a query multimedia package using a reference dataset of potentially related packages as evidences.

CORD19STS: COVID-19 Semantic Textual Similarity Dataset

no code implementations5 Jul 2020 Xiao Guo, Hengameh Mirzaalian, Ekraam Sabir, Ayush Jaiswal, Wael Abd-Almageed

To overcome this gap, we introduce CORD19STS dataset which includes 13, 710 annotated sentence pairs collected from COVID-19 open research dataset (CORD-19) challenge.

Information Retrieval Language Modelling +4

Discovery and Separation of Features for Invariant Representation Learning

no code implementations2 Dec 2019 Ayush Jaiswal, Rob Brekelmans, Daniel Moyer, Greg Ver Steeg, Wael Abd-Almageed, Premkumar Natarajan

Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization.

Representation Learning

Invariant Representations through Adversarial Forgetting

no code implementations11 Nov 2019 Ayush Jaiswal, Daniel Moyer, Greg Ver Steeg, Wael Abd-Almageed, Premkumar Natarajan

We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism.

NIESR: Nuisance Invariant End-to-end Speech Recognition

1 code implementation7 Jul 2019 I-Hung Hsu, Ayush Jaiswal, Premkumar Natarajan

Deep neural network models for speech recognition have achieved great success recently, but they can learn incorrect associations between the target and nuisance factors of speech (e. g., speaker identities, background noise, etc.

Speech Recognition

Unified Adversarial Invariance

no code implementations7 May 2019 Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan

We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions.

Disentanglement Fairness

Recurrent Convolutional Strategies for Face Manipulation Detection in Videos

1 code implementation2 May 2019 Ekraam Sabir, Jiaxin Cheng, Ayush Jaiswal, Wael Abd-Almageed, Iacopo Masi, Prem Natarajan

The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods.

Face Swapping Misinformation

RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial Invariance

no code implementations8 Mar 2019 Ayush Jaiswal, Shuai Xia, Iacopo Masi, Wael Abd-Almageed

For enterprise, personal and societal applications, there is now an increasing demand for automated authentication of identity from images using computer vision.

AIRD: Adversarial Learning Framework for Image Repurposing Detection

1 code implementation CVPR 2019 Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Iacopo Masi, Premkumar Natarajan

Image repurposing is a commonly used method for spreading misinformation on social media and online forums, which involves publishing untampered images with modified metadata to create rumors and further propaganda.


Unsupervised Adversarial Invariance

no code implementations NeurIPS 2018 Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan

Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from learning associations between these factors and the targets, thus reducing overfitting.

Data Augmentation Disentanglement +3

Large-Scale Unsupervised Deep Representation Learning for Brain Structure

no code implementations2 May 2018 Ayush Jaiswal, Dong Guo, Cauligi S. Raghavendra, Paul Thompson

Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data.

Representation Learning

CapsuleGAN: Generative Adversarial Capsule Network

1 code implementation17 Feb 2018 Ayush Jaiswal, Wael Abd-Almageed, Yue Wu, Premkumar Natarajan

We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models.

General Classification Semi-Supervised Image Classification

Bidirectional Conditional Generative Adversarial Networks

no code implementations20 Nov 2017 Ayush Jaiswal, Wael Abd-Almageed, Yue Wu, Premkumar Natarajan

Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$).

Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text

no code implementations6 Jul 2017 Ayush Jaiswal, Ekraam Sabir, Wael Abd-Almageed, Premkumar Natarajan

In this paper, we present a novel deep learning-based approach for assessing the semantic integrity of multimedia packages containing images and captions, using a reference set of multimedia packages.

Natural Language Processing Representation Learning

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