1 code implementation • 16 Mar 2020 • Moritz Kampelmühler, Axel Pinz
To enable an architecture to learn this highly abstract mapping, we employ the following key components: (1) a fully convolutional encoder-decoder structure, (2) a perceptual similarity loss function operating in an abstract feature space and (3) conditioning of the decoder on the label of the object that shall be sketched.
no code implementations • CVPR 2018 • Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes, Andrew Zisserman
In this paper, we shed light on deep spatiotemporal representations by visualizing what two-stream models have learned in order to recognize actions in video.
3 code implementations • ICCV 2017 • Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman
Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year.
1 code implementation • CVPR 2017 • Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes
Finally, our temporal ResNet boosts recognition performance and establishes a new state-of-the-art on dynamic scene recognition, as well as on the complementary task of action recognition.
1 code implementation • CVPR 2017 • Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes
This paper presents a general ConvNet architecture for video action recognition based on multiplicative interactions of spacetime features.
Ranked #47 on Action Recognition on HMDB-51
1 code implementation • NeurIPS 2016 • Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes
Two-stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos.
Ranked #48 on Action Recognition on UCF101
1 code implementation • CVPR 2016 • Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman
Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information.
Ranked #60 on Action Recognition on UCF101 (using extra training data)
Action Recognition In Videos Temporal Action Localization +1
no code implementations • CVPR 2015 • Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes
By using the resulting definition of saliency during feature pooling we show that action recognition performance achieves state-of-the-art levels on three widely considered action recognition datasets.
no code implementations • CVPR 2014 • Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes
This paper presents a unified bag of visual word (BoW) framework for dynamic scene recognition.
no code implementations • 1 Oct 2013 • Karla Brkić, Srđan Rašić, Axel Pinz, Siniša Šegvić, Zoran Kalafatić
This paper proposes combining spatio-temporal appearance (STA) descriptors with optical flow for human action recognition.