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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…
MaskConnect: Connectivity Learning by Gradient Descent
Karim Ahmed, Lorenzo Torresani
Although deep networks have recently emerged as the model of choice for many computer vision problems, in order to yield good results they often require time-consuming architecture…
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization
Bruno Korbar, Du Tran, Lorenzo Torresani
There is a natural correlation between the visual and auditive elements of a video. In this work we leverage this connection to learn general and effective models for both audio an…
Object Detection in Video with Spatiotemporal Sampling Networks
Gedas Bertasius, Lorenzo Torresani, Jianbo Shi
We propose a Spatiotemporal Sampling Network (STSN) that uses deformable convolutions across time for object detection in videos. Our STSN performs object detection in a video fram…