Search Results for author: Axel Pinz

Found 10 papers, 6 papers with code

Synthesizing human-like sketches from natural images using a conditional convolutional decoder

1 code implementation16 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.

Image Classification Translation

What have we learned from deep representations for action recognition?

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.

Action Recognition Temporal Action Localization

Detect to Track and Track to Detect

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.

Object object-detection +1

Temporal Residual Networks for Dynamic Scene Recognition

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.

Action Recognition Scene Recognition +1

Spatiotemporal Multiplier Networks for Video 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.

Action Recognition General Classification +1

Convolutional Two-Stream Network Fusion for Video Action Recognition

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

Dynamically Encoded Actions Based on Spacetime Saliency

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.

Action Recognition Temporal Action Localization

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