15 citations · 26 across the 5 of their papers we have counts for
9 papers
Compact CNN Structure Learning by Knowledge Distillation
Waqar Ahmed, Andrea Zunino, Pietro Morerio +1
The concept of compressing deep Convolutional Neural Networks (CNNs) is essential to use limited computation, power, and memory resources on embedded devices. However, existing met…
Explainable Deep Classification Models for Domain Generalization
Andrea Zunino, Sarah Adel Bargal, Riccardo Volpi +5
Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object…
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk +4
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reas…
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
Andrea Zunino, Sarah Adel Bargal, Pietro Morerio +3
We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction defined as the firing of neurons in specific paths. In this work, we utilize…
Excitation Backprop for RNNs
Sarah Adel Bargal, Andrea Zunino, Donghyun Kim +3
Deep models are state-of-the-art for many vision tasks including video action recognition and video captioning. Models are trained to caption or classify activity in videos, but li…
What Will I Do Next? The Intention from Motion Experiment
Andrea Zunino, Jacopo Cavazza, Atesh Koul +3
In computer vision, video-based approaches have been widely explored for the early classification and the prediction of actions or activities. However, it remains unclear whether t…
Revisiting Human Action Recognition: Personalization vs. Generalization
Andrea Zunino, Jacopo Cavazza, Vittorio Murino
By thoroughly revisiting the classic human action recognition paradigm, this paper aims at proposing a new approach for the design of effective action classification systems. Takin…
Predicting Human Intentions from Motion Only: A 2D+3D Fusion Approach
Andrea Zunino, Jacopo Cavazza, Atesh Koul +3
In this paper, we address the new problem of the prediction of human intents. There is neuro-psychological evidence that actions performed by humans are anticipated by peculiar mot…
Kernelized Covariance for Action Recognition
Jacopo Cavazza, Andrea Zunino, Marco San Biagio +1
In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only. We present a rigorous and…