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Found 6 papers, 0 papers with code
EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning
From a visual perception perspective, modern graphical user interfaces (GUIs) comprise a complex graphics-rich two-dimensional visuospatial arrangement of text, images, and interactive objects such as buttons and menus.
NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines
To the best of our knowledge, this is the first research paper showing a hybrid video codec that outperforms VVC on multiple datasets and multiple machine vision tasks.
Bridging the gap between image coding for machines and humans
Image coding for machines (ICM) aims at reducing the bitrate required to represent an image while minimizing the drop in machine vision analysis accuracy.
Leveraging progressive model and overfitting for efficient learned image compression
Nevertheless, the proposed LIC systems are still inferior to the state-of-the-art traditional techniques, for example, the Versatile Video Coding (VVC/H. 266) standard, due to either their compression performance or decoding complexity.
Learned Image Coding for Machines: A Content-Adaptive Approach
One possible solution approach consists of adapting current human-targeted image and video coding standards to the use case of machine consumption.
A Compact Deep Architecture for Real-time Saliency Prediction
Our proposed model consists of a modified U-net architecture, a novel fully connected layer, and central difference convolutional layers.
