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Found 8 papers, 1 papers with code
On the benefit of parameter-driven approaches for the modeling and the prediction of Satisfied User Ratio for compressed video
However, most of the previous works predict each point in SUR curve by using features both from source video and from compressed videos with assumption that the group-based JND annotations follow Gaussian distribution, which is neither practical nor accurate.
A Framework to Map VMAF with the Probability of Just Noticeable Difference between Video Encoding Recipes
To this end, in order to decouple encoding recipes and JND estimation, we propose a novel framework to map the difference of objective Video Quality Assessment (VQA) scores, i. e., VMAF, between two given videos encoded with different encoding recipes from the same content to the probability of having just noticeable difference between them.
Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest
Confinement during COVID-19 has caused serious effects on agriculture all over the world.
Accelerating Proposal Generation Network for \\Fast Face Detection on Mobile Devices
In this work, we present a proposal generation acceleration framework for real-time face detection.
RILOD: Near Real-Time Incremental Learning for Object Detection at the Edge
The key component of RILOD is a novel incremental learning algorithm that trains end-to-end for one-stage deep object detection models only using training data of new object classes.
Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML
Lifelong learning, the problem of continual learning where tasks arrive in sequence, has been lately attracting more attention in the computer vision community.
Talking Face Generation by Conditional Recurrent Adversarial Network
Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the entire video clip.
Boundary-sensitive Network for Portrait Segmentation
Compared to the general semantic segmentation problem, portrait segmentation has higher precision requirement on boundary area.
