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Found 9 papers, 2 papers with code
Estimating the Resize Parameter in End-to-end Learned Image Compression
By conducting extensive experimental tests on existing deep image compression models, we show results that our new resizing parameter estimation framework can provide Bj{\o}ntegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines.
Convolutional Block Design for Learned Fractional Downsampling
The layers of convolutional neural networks (CNNs) can be used to alter the resolution of their inputs, but the scaling factors are limited to integer values.
Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models
Video and image quality assessment has long been projected as a regression problem, which requires predicting a continuous quality score given an input stimulus.
Perceptually Optimizing Deep Image Compression
Mean squared error (MSE) and $\ell_p$ norms have largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties.
Multi-Agent Determinantal Q-Learning
Though practical, current methods rely on restrictive assumptions to decompose the centralized value function across agents for execution.
A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment
Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores.
Facial Emotion Recognition Using Deep Learning
We aim to construct a system that captures real-world facial images through the front camera on a laptop.
ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression
By building on top of an existing deep image compression model, we are able to demonstrate a bitrate reduction of as much as $31\%$ over MSE optimization, given a specified perceptual quality (VMAF) level.
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining.
Medical Named Entity Recognition
named-entity-recognition
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