Zellers, Rowan

Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages

Zadeh, A., Zellers, R., Pincus, E., & Morency, L.-P. (2016) · IEEE Intelligent Systems

Sentiment Analysis and Opinion MiningEmotion and Mood Recognition

Cited by 852

HellaSwag: Can a Machine Really Finish Your Sentence?

Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 830

From Recognition to Cognition: Visual Commonsense Reasoning

Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 769

Defending Against Neural Fake News

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019) · arXiv

Misinformation and Its ImpactsAdversarial Robustness in Machine Learning

Cited by 569

MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos

Zadeh, A., Zellers, R., Pincus, E., & Morency, L.-P. (2016) · arXiv

Sentiment Analysis and Opinion MiningEmotion and Mood Recognition

Cited by 350

GPT-4o System Card

OpenAI, :, Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 180

Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks

Lu, J., Clark, C., Zellers, R., Mottaghi, R., & Kembhavi, A. (2022) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 145

PIQA: Reasoning about Physical Commonsense in Natural Language

Bisk, Y., Zellers, R., Le bras, R., Gao, J., & Choi, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingMultimodal Machine Learning Applications

Cited by 37