7 papers · 1 filter
bViT: Investigating Single-Block Recurrence in Vision Transformers for Image Recognition
Michal Byra, Pawel Olszowiec, Grzegorz Stefanski +2
Vision Transformers (ViTs) are built by stacking independently parameterized blocks, but it remains unclear how much of this depth requires layer specific transformations and how m…
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
Gabriele Spadaro, Alberto Presta, Jhony H. Giraldo +5
Efficient compression of low-bit-rate point clouds is critical for bandwidth-constrained applications. However, existing techniques mainly focus on high-fidelity reconstruction, re…
Efficient Progressive Image Compression with Variance-aware Masking
Alberto Presta, Enzo Tartaglione, Attilio Fiandrotti +2
Learned progressive image compression is gaining momentum as it allows improved image reconstruction as more bits are decoded at the receiver. We propose a progressive image compre…
STanH : Parametric Quantization for Variable Rate Learned Image Compression
Alberto Presta, Enzo Tartaglione, Attilio Fiandrotti +1
In end-to-end learned image compression, encoder and decoder are jointly trained to minimize a cost function, where controls the trade-off between rate of the quanti…
Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection
Pengxi Zeng, Alberto Presta, Jonah Reinis +3
Efficient point cloud (PC) compression is crucial for streaming applications, such as augmented reality and cooperative perception. Classic PC compression techniques encode all the…
Domain Adaptation for Learned Image Compression with Supervised Adapters
Alberto Presta, Gabriele Spadaro, Enzo Tartaglione +2
In Learned Image Compression (LIC), a model is trained at encoding and decoding images sampled from a source domain, often outperforming traditional codecs on natural images; yet i…