most citedTransformer-based Image Generation from Scene Graphs

2 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG20231 cited

On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning

Lorenzo Bonicelli, Matteo Boschini, Emanuele Frascaroli +6

Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically. Continual Learning (CL) approaches seek to bridge this gap by facil…

cs.CV2023

A baseline on continual learning methods for video action recognition

Giulia Castagnolo, Concetto Spampinato, Francesco Rundo +2

Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervisedly-trained models. However, most…

cs.CV20232 cited

Transformer-based Image Generation from Scene Graphs

Renato Sortino, Simone Palazzo, Concetto Spampinato

Graph-structured scene descriptions can be efficiently used in generative models to control the composition of the generated image. Previous approaches are based on the combination…

cs.CV2023

TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation

Feiyan Hu, Simone Palazzo, Federica Proietto Salanitri +4

Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications. However, current solutions are p…

cs.LG2022

Transfer without Forgetting

Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello +5

This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretrainin…

cs.CV2022

Transforming Image Generation from Scene Graphs

Renato Sortino, Simone Palazzo, Concetto Spampinato

Generating images from semantic visual knowledge is a challenging task, that can be useful to condition the synthesis process in complex, subtle, and unambiguous ways, compared to…