728 citations · 1.7k across the 39 of their papers we have counts for
47 papers
TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization
Soumyaroop Nandi, Prem Natarajan, Wael Abd-Almageed
The evaluation datasets and metrics for image manipulation detection and localization (IMDL) research have been standardized. But the training dataset for such a task is still nons…
Alexa Arena: A User-Centric Interactive Platform for Embodied AI
Qiaozi Gao, Govind Thattai, Suhaila Shakiah +24
We introduce Alexa Arena, a user-centric simulation platform for Embodied AI (EAI) research. Alexa Arena provides a variety of multi-room layouts and interactable objects, for the…
MONet: Multi-scale Overlap Network for Duplication Detection in Biomedical Images
Ekraam Sabir, Soumyaroop Nandi, Wael AbdAlmageed +1
Manipulation of biomedical images to misrepresent experimental results has plagued the biomedical community for a while. Recent interest in the problem led to the curation of a dat…
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
Jack FitzGerald, Christopher Hench, Charith Peris +13
We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation. MASSIVE contains 1M real…
Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction
Kuan-Hao Huang, I-Hung Hsu, Premkumar Natarajan +2
We present a study on leveraging multilingual pre-trained generative language models for zero-shot cross-lingual event argument extraction (EAE). By formulating EAE as a language g…
SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentation
Jiaxin Cheng, Soumyaroop Nandi, Prem Natarajan +1
Unlike conventional zero-shot classification, zero-shot semantic segmentation predicts a class label at the pixel level instead of the image level. When solving zero-shot semantic…
Societal Biases in Language Generation: Progress and Challenges
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan +1
Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to comm…
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer
Jiaxin Cheng, Ayush Jaiswal, Yue Wu +2
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 widel…
Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations
Ying Lin, Han Wang, Jiangning Chen +5
The growing popularity of Virtual Assistants poses new challenges for Entity Resolution, the task of linking mentions in text to their referent entities in a knowledge base. Specif…
Discourse-level Relation Extraction via Graph Pooling
I-Hung Hsu, Xiao Guo, Premkumar Natarajan +1
The ability to capture complex linguistic structures and long-term dependencies among words in the passage is essential for discourse-level relation extraction (DRE) tasks. Graph n…
Class-agnostic Object Detection
Ayush Jaiswal, Yue Wu, Pradeep Natarajan +1
Object detection models perform well at localizing and classifying objects that they are shown during training. However, due to the difficulty and cost associated with creating and…
MEG: Multi-Evidence GNN for Multimodal Semantic Forensics
Ekraam Sabir, Ayush Jaiswal, Wael AbdAlmageed +1
Fake news often involves semantic manipulations across modalities such as image, text, location etc and requires the development of multimodal semantic forensics for its detection.…
"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan +1
Ad hominem attacks are those that target some feature of a person's character instead of the position the person is maintaining. These attacks are harmful because they propagate im…
Towards Controllable Biases in Language Generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan +1
We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to indu…
Discovery and Separation of Features for Invariant Representation Learning
Ayush Jaiswal, Rob Brekelmans, Daniel Moyer +3
Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization. We propose a framework for training robust ne…
Invariant Representations through Adversarial Forgetting
Ayush Jaiswal, Daniel Moyer, Greg Ver Steeg +2
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 mechan…
The Woman Worked as a Babysitter: On Biases in Language Generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan +1
We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this…
Report of 2017 NSF Workshop on Multimedia Challenges, Opportunities and Research Roadmaps
Shih-Fu Chang, Alex Hauptmann, Louis-Philippe Morency +18
With the transformative technologies and the rapidly changing global R&D landscape, the multimedia and multimodal community is now faced with many new opportunities and uncertainti…
NIESR: Nuisance Invariant End-to-end Speech Recognition
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…
Recurrent Convolutional Strategies for Face Manipulation Detection in Videos
Ekraam Sabir, Jiaxin Cheng, Ayush Jaiswal +3
The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods. De…
Unified Adversarial Invariance
Ayush Jaiswal, Yue Wu, Wael AbdAlmageed +1
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 ad…
QATM: Quality-Aware Template Matching For Deep Learning
Jiaxin Cheng, Yue Wu, Wael Abd-Almageed +1
Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc. We propose a novel quality-a…
AIRD: Adversarial Learning Framework for Image Repurposing Detection
Ayush Jaiswal, Yue Wu, Wael AbdAlmageed +2
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 cr…
Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network
Jiaxin Cheng, Yue Wu, Wael Abd-Almageed +1
The image-to-GPS verification problem asks whether a given image is taken at a claimed GPS location. In this paper, we treat it as an image verification problem -- whether a query…