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20192025
most citedLanguage Models with Image Descriptors are Strong Few-Shot Video-Language Learners

52 citations · 126 across the 30 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CV2023★ 2 cited

Video Summarization: Towards Entity-Aware Captions

Hammad A. Ayyubi, Tianqi Liu, Arsha Nagrani +7

Existing popular video captioning benchmarks and models deal with generic captions devoid of specific person, place or organization named entities. In contrast, news videos present…

cs.CL2023

Non-Sequential Graph Script Induction via Multimedia Grounding

Yu Zhou, Sha Li, Manling Li +4

Online resources such as WikiHow compile a wide range of scripts for performing everyday tasks, which can assist models in learning to reason about procedures. However, the scripts…

cs.CL2023

Language Models are Causal Knowledge Extractors for Zero-shot Video Question Answering

Hung-Ting Su, Yulei Niu, Xudong Lin +2

Causal Video Question Answering (CVidQA) queries not only association or temporal relations but also causal relations in a video. Existing question synthesis methods pre-trained qu…

cs.CV2023★ 3 cited

Supervised Masked Knowledge Distillation for Few-Shot Transformers

Han Lin, Guangxing Han, Jiawei Ma +3

Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features. However,…

cs.CV2023★ 1 cited

In Defense of Structural Symbolic Representation for Video Event-Relation Prediction

Andrew Lu, Xudong Lin, Yulei Niu +1

Understanding event relationships in videos requires a model to understand the underlying structures of events (i.e. the event type, the associated argument roles, and correspondin…