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20162026
most citedPredicting Human Eye Fixations via an LSTM-based Saliency Attentive Model

478 citations · 905 across the 72 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

cs.CV2021★ 1 cited

Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets

Marcella Cornia, Lorenzo Baraldi, Giuseppe Fiameni +1

This paper addresses the task of generating fluent descriptions by training on a non-uniform combination of data sources, containing both human-annotated and web-collected captions…

cs.RO2021★ 22 cited

Focus on Impact: Indoor Exploration with Intrinsic Motivation

Roberto Bigazzi, Federico Landi, Silvia Cascianelli +3

Exploration of indoor environments has recently experienced a significant interest, also thanks to the introduction of deep neural agents built in a hierarchical fashion and traine…

cs.LG2021★ 282 cited

Working Memory Connections for LSTM

Federico Landi, Lorenzo Baraldi, Marcella Cornia +1

Recurrent Neural Networks with Long Short-Term Memory (LSTM) make use of gating mechanisms to mitigate exploding and vanishing gradients when learning long-term dependencies. For t…

cs.CV2021★ 12 cited

From Show to Tell: A Survey on Deep Learning-based Image Captioning

Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi +3

Connecting Vision and Language plays an essential role in Generative Intelligence. For this reason, large research efforts have been devoted to image captioning, i.e. describing im…

cs.CV2021★ 8 cited

Learning to Select: A Fully Attentive Approach for Novel Object Captioning

Marco Cagrandi, Marcella Cornia, Matteo Stefanini +2

Image captioning models have lately shown impressive results when applied to standard datasets. Switching to real-life scenarios, however, constitutes a challenge due to the larger…

cs.CV2021★ 9 cited

Out of the Box: Embodied Navigation in the Real World

Roberto Bigazzi, Federico Landi, Marcella Cornia +3

The research field of Embodied AI has witnessed substantial progress in visual navigation and exploration thanks to powerful simulating platforms and the availability of 3D data of…

cs.CV2021★ 2 cited

Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis

Samuele Poppi, Marcella Cornia, Lorenzo Baraldi +1

As the request for deep learning solutions increases, the need for explainability is even more fundamental. In this setting, particular attention has been given to visualization te…