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Found 14 papers, 7 papers with code
Conditional Neural Video Coding with Spatial-Temporal Super-Resolution
This document is an expanded version of a one-page abstract originally presented at the 2024 Data Compression Conference.
Spy-Watermark: Robust Invisible Watermarking for Backdoor Attack
Backdoor attack aims to deceive a victim model when facing backdoor instances while maintaining its performance on benign data.
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations
COMpression with Bayesian Implicit NEural Representations (COMBINER) is a recent data compression method that addresses a key inefficiency of previous Implicit Neural Representation (INR)-based approaches: it avoids quantization and enables direct optimization of the rate-distortion performance.
Compression with Bayesian Implicit Neural Representations
Many common types of data can be represented as functions that map coordinates to signal values, such as pixel locations to RGB values in the case of an image.
NVTC: Nonlinear Vector Transform Coding
In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (R-D) performance.
Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression
By quantifying the decoding complexity as a factor in the optimization goal, we are now able to precisely control the RDC trade-off and then demonstrate how the rate-distortion performance of neural image codecs could adapt to various complexity demands.
Versatile Neural Processes for Learning Implicit Neural Representations
In this paper, we propose an efficient NP framework dubbed Versatile Neural Processes (VNP), which largely increases the capability of approximating functions.
Image Coding for Machines with Omnipotent Feature Learning
Learning a kind of feature that is both general (for AI tasks) and compact (for compression) is pivotal for its success.
Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression
Neural video codecs have demonstrated great potential in video transmission and storage applications.
Versatile Learned Video Compression
We show that the flow prediction module can largely reduce the transmission cost of voxel flows.
Soft then Hard: Rethinking the Quantization in Neural Image Compression
Quantization is one of the core components in lossy image compression.
Causal Contextual Prediction for Learned Image Compression
In this paper, we propose the concept of separate entropy coding to leverage a serial decoding process for causal contextual entropy prediction in the latent space.
Region Normalization for Image Inpainting
In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation.
Progressive Image Inpainting with Full-Resolution Residual Network
Recently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes.
