Paper Info
Title
CLIP-Event: Connecting Text and Images with Event Structures.
Abstract
Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-language pretraining models primarily focus on understanding objects in images or entities in text, they often ignore the alignment at the level of events and their argument structures. % In this work, we propose a contrastive learning framework to enforce vision-language pretraining models to comprehend events and associated argument (participant) roles. To achieve this, we take advantage of text information extraction technologies to obtain event structural knowledge, and utilize multiple prompt functions to contrast difficult negative descriptions by manipulating event structures. We also design an event graph alignment loss based on optimal transport to capture event argument structures. In addition, we collect a large event-rich dataset (106,875 images) for pretraining, which provides a more challenging image retrieval benchmark to assess the understanding of complicated lengthy sentences. Experiments show that our zero-shot CLIP-Event outperforms the state-of-the-art supervised model in argument extraction on Multimedia Event Extraction, achieving more than 5\% absolute F-score gain in event extraction, as well as significant improvements on a variety of downstream tasks under zero-shot settings.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.01593
IEEE Conference on Computer Vision and Pattern Recognition
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Manling Li187.89
Ruochen Xu2146.64
shuohang wang318016.63
luowei zhou4546.95
Xudong Lin552.77
Chenguang Zhu632822.92
Michael Zeng746.85
Heng Ji81544127.27
Shih-Fu Chang9130151101.53