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Emergency response missions depend on the fast relay of visual information, a task to which unmanned aerial vehicles are well adapted. However, the effective use of unmanned aerial vehicles is often compromised by bandwidth limitations that impede fast data transmission, thereby delaying the quick decision-making necessary in emergency situations. To address these challenges, this paper presents a streamlined hybrid annotation framework that utilizes the JPEG 2000 compression algorithm to facilitate object detection under limited bandwidth.

In this work, we propose a semi-supervised deep feature generation network that accounts for local similarities. It is based on the deep dictionary learning (DDL) framework. The formulation accounts for two unique aspects of hyperspectral classification. First, the fact that the total number of pixels / samples to be labeled is constant; this allows for a semi-supervised formulation allowing only a few pixels / samples to be labeled as training data. Second, the samples / pixels are spatially correlated; this leads to a graph regularization formulation.

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.