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Wavelet transforms have been a relevant topic in signal processing for many years. One of the most common strategies when designing wavelet transforms is the use of lifting schemes, known for their perfect reconstruction properties and flexible design. This paper introduces a novel 2D non-separable lifting design methodology based on deep learning architectures. The proposed method is assessed within the context of end-to-end lossless image compression.

We revisit language bottleneck models as an approach to ensuring the explainability of deep learning models for image classification. Because of inevitable information loss incurred in the step of converting images into language, the accuracy of language bottleneck models is considered to be inferior to that of standard black-box models. Recent image captioners based on large-scale foundation models of Vision and Language, however, have the ability to accurately describe images in verbal detail to a degree that was previously believed to not be realistically possible.

Counting repetitive actions in long untrimmed videos is a challenging task that has many applications such as rehabilitation.

Deepfakes represent one of the toughest challenges in the world of Cybersecurity and Digital Forensics, especially considering the high-quality results obtained with recent generative AI-based solutions. Almost all generative models leave unique traces in synthetic data that, if analyzed and identified in detail, can be exploited to improve the generalization limitations of existing deepfake detectors.

Multi-class multi-instance segmentation is the task of identifying masks for multiple object classes and multiple instances of the same class within an image. The Segment Anything Model (SAM) is a new foundation model designed for promptable multi-class multi-instance segmentation. SAM is able to segment objects in any image using a pre-defined point grid as an input prompt in the ``everything'' mode. However, out of the box SAM tends to output part or sub-part segmentation masks (under-segmentation) in different real-world applications.