TL;DR
A comprehensive Ensemble Learning framework designed to enhance deepfake detection capabilities on the CIFAKE dataset and confirms that leveraging architectural diversity is more effective than relying on complex single models, offering an optimal balance between detection accuracy and computational efficiency.
Content-based image retrieval (CBIR) has become an essential tool in several industries, allowing images to be searched efficiently based on their properties. In the context of digital rock studies, a CBIR system assists, for example, in applications in the oil and gas industry, where the identification and categorizat...
OL-RGB is an input representation that amplifies inter-region contrast without extra sensors, extra annotation, or any change to the network, and is on par with recent transformer-based detectors that need one to two orders of magnitude more inference parameters, although it remains below the strongest diffusion-based...
This model leverages different layers of the large language model (LLM) decoder, treating them as multiple detectors that perform synchronous distortion detection using multi-level features, which helps mitigate the ambiguous foreground-background separation commonly encountered in the S2A problem.
This work integrates selective State Space Model mechanisms from Mamba and three approximate self-attention methods, including Longformer, LongNet, and Nyströmformer, into a regularization-based reconstruction network that enables efficient modeling of long-range dependencies using modern sequence-processing architectu...
This work validates the design effectiveness of decoupling global and local representations within a frozen backbone, and establishes a new baseline for parameter-efficient enhancement.
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