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Found 6 papers, 1 papers with code
Interactive Face Video Coding: A Generative Compression Framework
In this paper, we propose a novel framework for Interactive Face Video Coding (IFVC), which allows humans to interact with the intrinsic visual representations instead of the signals.
Just Noticeable Difference Modeling for Face Recognition System
In particular, in order to maximum redundancy removal without impairment of robust identity information, we apply the encoder with multiple feature extraction and attention-based feature decomposition modules to progressively decompose face features into two uncorrelated components, i. e., identity and residual features, via self-supervised learning.
End-to-end Compression Towards Machine Vision: Network Architecture Design and Optimization
In this paper, we show that the design and optimization of network architecture could be further improved for compression towards machine vision.
Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression
In particular, we study the feature and texture compression in a scalable coding framework, where the base layer serves as the deep learning feature and enhancement layer targets to perfectly reconstruct the texture.
End-to-End Facial Deep Learning Feature Compression with Teacher-Student Enhancement
In this paper, we propose a novel end-to-end feature compression scheme by leveraging the representation and learning capability of deep neural networks, towards intelligent front-end equipped analysis with promising accuracy and efficiency.
Scalable Facial Image Compression with Deep Feature Reconstruction
In this paper, we propose a scalable image compression scheme, including the base layer for feature representation and enhancement layer for texture representation.
