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20222024
most citedTransformer-based Image Generation from Scene Graphs

2 citations · 3 across the 10 of their papers we have counts for

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8 papers · 1 filter

cs.CV2024

SalFoM: Dynamic Saliency Prediction with Video Foundation Models

Morteza Moradi, Mohammad Moradi, Francesco Rundo +3

Recent advancements in video saliency prediction (VSP) have shown promising performance compared to the human visual system, whose emulation is the primary goal of VSP. However, cu…

cs.CV2024

Diffexplainer: Towards Cross-modal Global Explanations with Diffusion Models

Matteo Pennisi, Giovanni Bellitto, Simone Palazzo +2

We present DiffExplainer, a novel framework that, leveraging language-vision models, enables multimodal global explainability. DiffExplainer employs diffusion models conditioned on…

cs.CV2024

Selective Attention-based Modulation for Continual Learning

Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi +5

We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting. Inspired by neurophysiolog…

cs.CV2024

Transformer-based Video Saliency Prediction with High Temporal Dimension Decoding

Morteza Moradi, Simone Palazzo, Concetto Spampinato

In recent years, finding an effective and efficient strategy for exploiting spatial and temporal information has been a hot research topic in video saliency prediction (VSP). With…

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…