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Found 5 papers, 1 papers with code
NERV++: An Enhanced Implicit Neural Video Representation
Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a low memory footprint.
AICT: An Adaptive Image Compression Transformer
Motivated by the efficiency investigation of the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose to enhance the latter, as first, with a more straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute image compression transformer (ICT).
ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression
Over the last few years, neural image compression has gained wide attention from research and industry, yielding promising end-to-end deep neural codecs outperforming their conventional counterparts in rate-distortion performance.
Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient Neural Image Compression
Recently, the performance of neural image compression (NIC) has steadily improved thanks to the last line of study, reaching or outperforming state-of-the-art conventional codecs.
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images
The quality of patient care associated with diagnostic radiology is proportionate to a physician workload.
