109 citations · 165 across the 12 of their papers we have counts for
19 papers
Seamless Interaction: Dyadic Audiovisual Motion Modeling and Large-Scale Dataset
Vasu Agrawal, Akinniyi Akinyemi, Kathryn Alvero +81
Human communication involves a complex interplay of verbal and nonverbal signals, essential for conveying meaning and achieving interpersonal goals. To develop socially intelligent…
Approximate Neural Architecture Search via Operation Distribution Learning
Xingchen Wan, Binxin Ru, Pedro M. Esperança +1
The standard paradigm in Neural Architecture Search (NAS) is to search for a fully deterministic architecture with specific operations and connections. In this work, we instead pro…
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family
Roy Henha Eyono, Fabio Maria Carlucci, Pedro M Esperança +2
State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models. However, widespread use is constrained by device hardware limi…
OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression
Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci +1
Explicit deep generative models (DGMs), e.g., VAEs and Normalizing Flows, have shown to offer an effective data modelling alternative for lossless compression. However, DGMs themse…
Adversarial Branch Architecture Search for Unsupervised Domain Adaptation
Luca Robbiano, Muhammad Rameez Ur Rahman, Fabio Galasso +2
Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as it allows to bridge different visual domains enabling robust performances in the real world. To date,…
Self-Supervised Learning Across Domains
Silvia Bucci, Antonio D'Innocente, Yujun Liao +3
Human adaptability relies crucially on learning and merging knowledge from both supervised and unsupervised tasks: the parents point out few important concepts, but then the childr…
Neural Architecture Generator Optimization
Binxin Ru, Pedro Esperanca, Fabio Carlucci
Neural Architecture Search (NAS) was first proposed to achieve state-of-the-art performance through the discovery of new architecture patterns, without human intervention. An over-…
NAS evaluation is frustratingly hard
Antoine Yang, Pedro M. Esperança, Fabio M. Carlucci
Neural Architecture Search (NAS) is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012. Despite many great works lea…
MANAS: Multi-Agent Neural Architecture Search
Vasco Lopes, Fabio Maria Carlucci, Pedro M Esperança +6
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a g…
Domain Generalization by Solving Jigsaw Puzzles
Fabio Maria Carlucci, Antonio D'Innocente, Silvia Bucci +2
Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but th…
Learning to see across Domains and Modalities
Fabio Maria Carlucci
Deep learning has raised hopes and expectations as a general solution for many applications; indeed it has proven effective, but it also showed a strong dependence on large quantit…
Hallucinating Agnostic Images to Generalize Across Domains
Fabio M. Carlucci, Paolo Russo, Tatiana Tommasi +1
The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems. Although many training sources may be available in real contexts, t…
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition
Antonio D'Innocente, Fabio Maria Carlucci, Mirco Colosi +1
Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem. The most successful convolutional archi…
From source to target and back: symmetric bi-directional adaptive GAN
Paolo Russo, Fabio Maria Carlucci, Tatiana Tommasi +1
The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervis…
AutoDIAL: Automatic DomaIn Alignment Layers
Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo +2
Classifiers trained on given databases perform poorly when tested on data acquired in different settings. This is explained in domain adaptation through a shift among distributions…
(DE)^2 CO: Deep Depth Colorization
F. M. Carlucci, P. Russo, B. Caputo
The ability to classify objects is fundamental for robots. Besides knowledge about their visual appearance, captured by the RGB channel, robots heavily need also depth information…
Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation
Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo +2
The empirical fact that classifiers, trained on given data collections, perform poorly when tested on data acquired in different settings is theoretically explained in domain adapt…
A deep representation for depth images from synthetic data
Fabio Maria Carlucci, Paolo Russo, Barbara Caputo
Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D dat…
When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks
Ilja Kuzborskij, Fabio Maria Carlucci, Barbara Caputo
Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbour (NBNN)-based classifiers have lost momentu…