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Guglielmo Camporese

5 accepted papers

2024

Distilling Knowledge for Short-to-Long Term Trajectory Prediction

IROS 2024

Long-term trajectory forecasting is an important and challenging problem in the fields of computer vision, machine learning, and robotics. One fundamental difficulty stands in the evolution of the trajectory that becomes more and more uncertain and unpredictable as the time horizon grows, subsequent

Cited by 5SourceScholar
2023

Empowering Convolutional Neural Nets with MetaSin Activation

NeurIPS 2023poster

ReLU networks have remained the default choice for models in the area of image prediction despite their well-established spectral bias towards learning low frequencies faster, and consequently their difficulty of reproducing high frequency visual details. As an alternative, sin networks showed promi…

Cited by 1SourcePDFScholar
2023

TAMformer: Multi-Modal Transformer with Learned Attention Mask for Early Intent Prediction

ICASSP 2023accepted

Human intention prediction is a growing area of research where an activity in a video has to be anticipated by a vision-based system. To this end, the model creates a representation of the past, and subsequently, it produces future hypotheses about upcoming scenarios. In this work, we focus on pedes…

Cited by 0SourceScholar
2021

Conditional Variational Capsule Network for Open Set Recognition

ICCV 2021poster

In open set recognition, a classifier has to detect unknown classes that are not known at training time. In order to recognize new categories, the classifier has to project the input samples of known classes in very compact and separated regions of the features space for discriminating samples of un…

Cited by 66PDFcodeScholar
2021

Improved Robustness to Disfluencies in Rnn-Transducer Based Speech Recognition

ICASSP 2021accepted

Automatic Speech Recognition (ASR) based on Recurrent Neural Network Transducers (RNN-T) is gaining interest in the speech community. We investigate data selection and preparation choices aiming for improved robustness of RNN-T ASR to speech disfluencies with a focus on partial words. For evaluation…

Cited by 0SourceScholar