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Author Pau Rodriguez; Guillem Cucurull; Jordi Gonzalez; Josep M. Gonfaus; Kamal Nasrollahi; Thomas B. Moeslund; Xavier Roca edit   pdf
doi  openurl
  Title Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification Type Journal Article
  Year 2017 Publication IEEE Transactions on cybernetics Abbreviated Journal Cyber  
  Volume Issue Pages (down) 1-11  
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  Abstract Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database.  
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  Notes ISE; 600.119; 600.098 Approved no  
  Call Number Admin @ si @ RCG2017a Serial 2926  
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Author Diego Velazquez; Pau Rodriguez; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez edit  url
openurl 
  Title A Closer Look at Embedding Propagation for Manifold Smoothing Type Journal Article
  Year 2022 Publication Journal of Machine Learning Research Abbreviated Journal JMLR  
  Volume 23 Issue 252 Pages (down) 1-27  
  Keywords Regularization; emi-supervised learning; self-supervised learning; adversarial robustness; few-shot classification  
  Abstract Supervised training of neural networks requires a large amount of manually annotated data and the resulting networks tend to be sensitive to out-of-distribution (OOD) data.
Self- and semi-supervised training schemes reduce the amount of annotated data required during the training process. However, OOD generalization remains a major challenge for most methods. Strategies that promote smoother decision boundaries play an important role in out-of-distribution generalization. For example, embedding propagation (EP) for manifold smoothing has recently shown to considerably improve the OOD performance for few-shot classification. EP achieves smoother class manifolds by building a graph from sample embeddings and propagating information through the nodes in an unsupervised manner. In this work, we extend the original EP paper providing additional evidence and experiments showing that it attains smoother class embedding manifolds and improves results in settings beyond few-shot classification. Concretely, we show that EP improves the robustness of neural networks against multiple adversarial attacks as well as semi- and
self-supervised learning performance.		  
  Address 9/2022  
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  Notes ISE Approved no  
  Call Number Admin @ si @ VRG2022 Serial 3762  
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Author Wenjuan Gong; Yue Zhang; Wei Wang; Peng Cheng; Jordi Gonzalez edit  url
openurl 
  Title Meta-MMFNet: Meta-learning-based Multi-model Fusion Network for Micro-expression Recognition Type Journal Article
  Year 2023 Publication ACM Transactions on Multimedia Computing, Communications, and Applications Abbreviated Journal TMCCA  
  Volume 20 Issue 2 Pages (down) 1–20  
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  Abstract Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning-based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method.  
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  Call Number Admin @ si @ GZW2023 Serial 3862  
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Author Jordi Gonzalez; Javier Varona; Xavier Roca; Juan J. Villanueva edit  openurl
  Title A Comparison Framework for Walking Performances using aSpaces Type Journal
  Year 2005 Publication Electronic Letters on Computer Vision and Image Analysis, Special Issue on articulated Motion, 5(3):105–116 (Electronic Letters: IF: 1.016) Abbreviated Journal  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ GVR2005 Serial 623  
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Author Mikhail Mozerov; V. Kober edit  openurl
  Title Impulse Noise Removal with Gradient Adaptive Neighborhoods Type Journal
  Year 2006 Publication Optical Engineering, 45: 67003 Abbreviated Journal  
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  Notes ISE Approved no  
  Call Number ISE @ ise @ MoK2006 Serial 676  
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