14 citations · 14 across the 4 of their papers we have counts for
4 papers
GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling
Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham +2
Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the en…
Improving Event Definition Following For Zero-Shot Event Detection
Zefan Cai, Po-Nien Kung, Ashima Suvarna +6
Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches…
VideoCon: Robust Video-Language Alignment via Contrast Captions
Hritik Bansal, Yonatan Bitton, Idan Szpektor +2
Despite being (pre)trained on a massive amount of data, state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video c…
Leaving Reality to Imagination: Robust Classification via Generated Datasets
Hritik Bansal, Aditya Grover
Recent research on robustness has revealed significant performance gaps between neural image classifiers trained on datasets that are similar to the test set, and those that are fr…