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Found 6 papers, 3 papers with code
Latent Bernoulli Autoencoder
In this work, we pose a question whether it is possible to design and train an autoencoder model in an end-to-end fashion to learn latent representations in multivariate Bernoulli space, and achieve performance comparable with the current state-of-the-art variational methods.
Summarizing Videos with Attention
In this work we propose a novel method for supervised, keyshots based video summarization by applying a conceptually simple and computationally efficient soft, self-attention mechanism.
Ranked #3 on
Video Summarization
on TvSum
(using extra training data)
Superframes, A Temporal Video Segmentation
The goal of video segmentation is to turn video data into a set of concrete motion clusters that can be easily interpreted as building blocks of the video.
AMNet: Memorability Estimation with Attention
Further on we study the impact of the attention mechanism on the memorability estimation and evaluate our network on the SUN Memorability and the LaMem datasets.
Detection of Salient Regions in Crowded Scenes
The increasing number of cameras and a handful of human operators to monitor the video inputs from hundreds of cameras leave the system ill equipped to fulfil the task of detecting anomalies.
Refined Particle Swarm Intelligence Method for Abrupt Motion Tracking
Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or an accurate motion model.
