361 citations · 944 across the 48 of their papers we have counts for
6 papers · 1 filter
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…
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
Shahab Aslani, Michael Dayan, Loredana Storelli +4
In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-t…
Learning with privileged information via adversarial discriminative modality distillation
Nuno C. Garcia, Pietro Morerio, Vittorio Murino
Heterogeneous data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while training data can b…
Modality Distillation with Multiple Stream Networks for Action Recognition
Nuno Garcia, Pietro Morerio, Vittorio Murino
Diverse input data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while a (training) datase…
Generalizing to Unseen Domains via Adversarial Data Augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener +3
We are concerned with learning models that generalize well to different \emph{unseen} domains. We consider a worst-case formulation over data distributions that are near the source…
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…