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Efficient Movie Scene Detection using State-Space Transformers
Md Mohaiminul Islam, Mahmudul Hasan, Kishan Shamsundar Athrey +2
The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as…
Vision Transformers are Parameter-Efficient Audio-Visual Learners
Yan-Bo Lin, Yi-Lin Sung, Jie Lei +2
Vision transformers (ViTs) have achieved impressive results on various computer vision tasks in the last several years. In this work, we study the capability of frozen ViTs, pretra…
VindLU: A Recipe for Effective Video-and-Language Pretraining
Feng Cheng, Xizi Wang, Jie Lei +3
The last several years have witnessed remarkable progress in video-and-language (VidL) understanding. However, most modern VidL approaches use complex and specialized model archite…
SimpleClick: Interactive Image Segmentation with Simple Vision Transformers
Qin Liu, Zhenlin Xu, Gedas Bertasius +1
Click-based interactive image segmentation aims at extracting objects with a limited user clicking. A hierarchical backbone is the de-facto architecture for current methods. Recent…
MuMUR : Multilingual Multimodal Universal Retrieval
Avinash Madasu, Estelle Aflalo, Gabriela Ben Melech Stan +4
Multi-modal retrieval has seen tremendous progress with the development of vision-language models. However, further improving these models require additional labelled data which is…
Object State Change Classification in Egocentric Videos using the Divided Space-Time Attention Mechanism
Md Mohaiminul Islam, Gedas Bertasius
This report describes our submission called "TarHeels" for the Ego4D: Object State Change Classification Challenge. We use a transformer-based video recognition model and leverage…