SOTAVerified

Video Understanding

A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.

Source: Action Detection from a Robot-Car Perspective

Papers

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Showing 1071–1080 of 1149 papers

TitleStatusHype
Efficient Video Understanding via Layered Multi Frame-Rate Analysis—0
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video ClassificationCode0
Random Temporal Skipping for Multirate Video Analysis—0
Morph: Flexible Acceleration for 3D CNN-based Video Understanding—0
Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors from ImagesCode0
Representation Flow for Action RecognitionCode0
Learnable Pooling Methods for Video ClassificationCode0
Non-local NetVLAD Encoding for Video Classification—0
Large-Scale Video Classification with Feature Space Augmentation coupled with Learned Label Relations and Ensembling—0
Label Denoising with Large Ensembles of Heterogeneous Neural Networks—0
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