9 citations · 9 across the 5 of their papers we have counts for
6 papers · 1 filter
ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling
Francesca Babiloni, Alexandros Lattas, Jiankang Deng +1
We propose ID-to-3D, a method to generate identity- and text-guided 3D human heads with disentangled expressions, starting from even a single casually captured in-the-wild image of…
Tunable Convolutions with Parametric Multi-Loss Optimization
Matteo Maggioni, Thomas Tanay, Francesca Babiloni +2
Behavior of neural networks is irremediably determined by the specific loss and data used during training. However it is often desirable to tune the model at inference time based o…
Poly-NL: Linear Complexity Non-local Layers with Polynomials
Francesca Babiloni, Ioannis Marras, Filippos Kokkinos +3
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all poss…
Exploring the Challenges towards Lifelong Fact Learning
Mohamed Elhoseiny, Francesca Babiloni, Rahaf Aljundi +3
So far life-long learning (LLL) has been studied in relatively small-scale and relatively artificial setups. Here, we introduce a new large-scale alternative. What makes the propos…
Memory Aware Synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny +2
Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from b…
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
Nizar Massouh, Francesca Babiloni, Tatiana Tommasi +3
Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification.…