18 citations · 31 across the 4 of their papers we have counts for
9 papers · 1 filter
Is Space-Time Attention All You Need for Video Understanding?
Gedas Bertasius, Heng Wang, Lorenzo Torresani
We present a convolution-free approach to video classification built exclusively on self-attention over space and time. Our method, named "TimeSformer," adapts the standard Transfo…
VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs
Xudong Lin, Gedas Bertasius, Jue Wang +3
We present \textsc{Vx2Text}, a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. In order to leverage transformer networks, whic…
COBE: Contextualized Object Embeddings from Narrated Instructional Video
Gedas Bertasius, Lorenzo Torresani
Many objects in the real world undergo dramatic variations in visual appearance. For example, a tomato may be red or green, sliced or chopped, fresh or fried, liquid or solid. Trai…
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
Gedas Bertasius, Christoph Feichtenhofer, Du Tran +2
Modern approaches for multi-person pose estimation in video require large amounts of dense annotations. However, labeling every frame in a video is costly and labor intensive. To r…
Attentive Action and Context Factorization
Yang Wang, Vinh Tran, Gedas Bertasius +2
We propose a method for human action recognition, one that can localize the spatiotemporal regions that `define' the actions. This is a challenging task due to the subtlety of huma…
Learning Discriminative Motion Features Through Detection
Gedas Bertasius, Christoph Feichtenhofer, Du Tran +2
Despite huge success in the image domain, modern detection models such as Faster R-CNN have not been used nearly as much for video analysis. This is arguably due to the fact that d…