Search Results for author: Francesca Babiloni

Found 6 papers, 2 papers with code

Poly-NL: Linear Complexity Non-local Layers with Polynomials

no code implementations6 Jul 2021 Francesca Babiloni, Ioannis Marras, Filippos Kokkinos, Jiankang Deng, Grigorios Chrysos, Stefanos Zafeiriou

Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.

Face Detection Instance Segmentation +1

Poly-NL: Linear Complexity Non-Local Layers With 3rd Order Polynomials

no code implementations ICCV 2021 Francesca Babiloni, Ioannis Marras, Filippos Kokkinos, Jiankang Deng, Grigorios Chrysos, Stefanos Zafeiriou

Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.

Face Detection Instance Segmentation +1

TESA: Tensor Element Self-Attention via Matricization

no code implementations CVPR 2020 Francesca Babiloni, Ioannis Marras, Gregory Slabaugh, Stefanos Zafeiriou

Representation learning is a fundamental part of modern computer vision, where abstract representations of data are encoded as tensors optimized to solve problems like image segmentation and inpainting.

Image Inpainting Image Segmentation +3

Exploring the Challenges towards Lifelong Fact Learning

no code implementations26 Dec 2018 Mohamed Elhoseiny, Francesca Babiloni, Rahaf Aljundi, Marcus Rohrbach, Manohar Paluri, Tinne Tuytelaars

So far life-long learning (LLL) has been studied in relatively small-scale and relatively artificial setups.

Memory Aware Synapses: Learning what (not) to forget

2 code implementations ECCV 2018 Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, Tinne Tuytelaars

We show state-of-the-art performance and, for the first time, the ability to adapt the importance of the parameters based on unlabeled data towards what the network needs (not) to forget, which may vary depending on test conditions.

Object Recognition

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

1 code implementation IEEE Xplore: 2017 Nizar Massouh, Francesca Babiloni, Tatiana Tommasi, Jay Young, Nick Hawes, Barbara Caputo

We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy.

Object Categorization Object Discovery

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