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Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
Efficient compression of low-bit-rate point clouds is critical for bandwidth-constrained applications. However, existing techniques …
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Efficient Federated Model Aggregation through Neural Velocity
Aggregation methods in Federated Learning (FL) play a fundamental role in the performance and convergence of the global model. In this …
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Efficient Progressive Image Compression with Variance-Aware Masking
Learned progressive image compression is gaining momentum as it allows improved image reconstruction as more bits are decoded at the …
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WiGNet: Windowed Vision Graph Neural Network
In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with …
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AI-Assisted Diagnosis for Covid-19 CXR Screening: from Data Collection to Clinical Validation
In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took …
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ALICE: Adapt your Learnable Image Compression modEl for variable bitrates
When training a Learned Image Compression model, the loss function is minimized such that the encoder and the decoder attain a target …
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Analysing the social network dynamics of dairy cows with computer vision
The structured social hierarchies and individual social bonds within dairy cows’ groups significantly influence their welfare and …
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Domain Adaptation for Learned Image Compression with Supervised Adapters
In Learned Image Compression (LIC), a model is trained at encoding and decoding images sampled from a source domain, often …
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GABIC: GRAPH-BASED ATTENTION BLOCK FOR IMAGE COMPRESSION
While standardized codecs like JPEG and HEVC-intra represent the industry standard in image compression, neural Learned Image …
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A round-trip journey in pruned artificial neural networks
In the last decade, deep learning models competed for performance at the price of tremendous computational costs. Such a critical …
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