18 citations · 22 across the 2 of their papers we have counts for
8 papers
Hierarchical Residual Attention Network for Single Image Super-Resolution
Parichehr Behjati, Pau Rodriguez, Armin Mehri +3
Convolutional neural networks are the most successful models in single image super-resolution. Deeper networks, residual connections, and attention mechanisms have further improved…
OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network
Parichehr Behjati, Pau Rodriguez, Armin Mehri +3
Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-…
Pay attention to the activations: a modular attention mechanism for fine-grained image recognition
Pau Rodríguez López, Diego Velazquez Dorta, Guillem Cucurull Preixens +3
Fine-grained image recognition is central to many multimedia tasks such as search, retrieval and captioning. Unfortunately, these tasks are still challenging since the appearance o…
From 2D to 3D Geodesic-based Garment Matching
Meysam Madadi, Egils Avots, Sergio Escalera +3
A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is ma…
Attend and Rectify: a Gated Attention Mechanism for Fine-Grained Recovery
Pau Rodríguez, Josep M. Gonfaus, Guillem Cucurull +2
We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiri…
Beyond One-hot Encoding: lower dimensional target embedding
Pau Rodríguez, Miguel A. Bautista, Jordi Gonzàlez +1
Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, One-hot encoding is the most prevalent strategy due to its simplicity. However, thi…