478 citations · 1.5k across the 97 of their papers we have counts for
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Meshed-Memory Transformer for Image Captioning
Marcella Cornia, Matteo Stefanini, Lorenzo Baraldi +1
Transformer-based architectures represent the state of the art in sequence modeling tasks like machine translation and language understanding. Their applicability to multi-modal co…
Video action detection by learning graph-based spatio-temporal interactions
Matteo Tomei, Lorenzo Baraldi, Simone Calderara +2
Action Detection is a complex task that aims to detect and classify human actions in video clips. Typically, it has been addressed by processing fine-grained features extracted fro…
Multimodal Attention Networks for Low-Level Vision-and-Language Navigation
Federico Landi, Lorenzo Baraldi, Marcella Cornia +2
Vision-and-Language Navigation (VLN) is a challenging task in which an agent needs to follow a language-specified path to reach a target destination. The goal gets even harder as t…
SMArT: Training Shallow Memory-aware Transformers for Robotic Explainability
Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
The ability to generate natural language explanations conditioned on the visual perception is a crucial step towards autonomous agents which can explain themselves and communicate…
Warp and Learn: Novel Views Generation for Vehicles and Other Objects
Andrea Palazzi, Luca Bergamini, Simone Calderara +1
In this work we introduce a new self-supervised, semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image. Differently from paramet…
Embodied Vision-and-Language Navigation with Dynamic Convolutional Filters
Federico Landi, Lorenzo Baraldi, Massimiliano Corsini +1
In Vision-and-Language Navigation (VLN), an embodied agent needs to reach a target destination with the only guidance of a natural language instruction. To explore the environment…
M-VAD Names: a Dataset for Video Captioning with Naming
Stefano Pini, Marcella Cornia, Federico Bolelli +2
Current movie captioning architectures are not capable of mentioning characters with their proper name, replacing them with a generic "someone" tag. The lack of movie description d…
Classifying Signals on Irregular Domains via Convolutional Cluster Pooling
Angelo Porrello, Davide Abati, Simone Calderara +1
We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset. To this end, we intr…
Anomaly Locality in Video Surveillance
Federico Landi, Cees G. M. Snoek, Rita Cucchiara
This paper strives for the detection of real-world anomalies such as burglaries and assaults in surveillance videos. Although anomalies are generally local, as they happen in a lim…
Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face
Matteo Fabbri, Guido Borghi, Fabio Lanzi +3
Can faces acquired by low-cost depth sensors be useful to catch some characteristic details of the face? Typically the answer is no. However, new deep architectures can generate RG…
Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?
Federico Fulgeri, Matteo Fabbri, Stefano Alletto +2
When you see a person in a crowd, occluded by other persons, you miss visual information that can be used to recognize, re-identify or simply classify him or her. You can imagine i…