6 citations · 13 across the 7 of their papers we have counts for
7 papers
Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization
Federico Fontana, Anxhelo Diko, Romeo Lanzino +4
The rapid evolution of deepfake generation technologies poses critical challenges for detection systems, as non-continual learning methods demand frequent and expensive retraining.…
Semantically Guided Action Anticipation
Anxhelo Diko, Antonino Furnari, Luigi Cinque +1
Unsupervised domain adaptation remains a critical challenge in enabling the knowledge transfer of models across unseen domains. Existing methods struggle to balance the need for do…
ReWind: Understanding Long Videos with Instructed Learnable Memory
Anxhelo Diko, Tinghuai Wang, Wassim Swaileh +2
Vision-Language Models (VLMs) are crucial for applications requiring integrated understanding textual and visual information. However, existing VLMs struggle with long videos due t…
CycleBNN: Cyclic Precision Training in Binary Neural Networks
Federico Fontana, Romeo Lanzino, Anxhelo Diko +2
This paper works on Binary Neural Networks (BNNs), a promising avenue for efficient deep learning, offering significant reductions in computational overhead and memory footprint to…
Semantically Guided Representation Learning For Action Anticipation
Anxhelo Diko, Danilo Avola, Bardh Prenkaj +2
Action anticipation is the task of forecasting future activity from a partially observed sequence of events. However, this task is exposed to intrinsic future uncertainty and the d…
Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks
Lanzino Romeo, Fontana Federico, Diko Anxhelo +2
Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need f…
ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual Connections
Anxhelo Diko, Danilo Avola, Marco Cascio +1
Vision Transformer (ViT) self-attention mechanism is characterized by feature collapse in deeper layers, resulting in the vanishing of low-level visual features. However, such feat…