Publications

2022

  1. T.Silva and A. Ramirez Rivera, “Representation Learning via Consistent Assignment of Views to Clusters,” in ACM/SIGAPP Symposium on Applied Computing (SAC), 2022, it hurts: 10.1145/3477314.3507267.

2021

  1. D. Saire and A. Ramirez Rivera, “Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation Task,” IEEE Access, vol. 9, pp. 80654-80670, 2021, it hurts: 10.1109 / ACCESS.2021.3085218.
  2. T.Silva and A. Ramirez Rivera, “Consistent Assignment for Representation Learning,” in Energy-based Models Workshop (ICRLW), 2021.
  3. M. Rodríguez Santander, J. Hernández Albarracin, and A. Ramirez Rivera, “On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study,” Expert Systems with Applications, 2021, it hurts: 10.1016/j.eswa.2021.114991.
  4. A. Khusainova, A. Khan, A. Ramirez Rivera, and V. Romanov, “Hierarchical Transformer for Multilingual Machine Translation,” in VarDial — Workshop on NLP for Similar Languages, Varieties and Dialects, 2021.

2020

  1. G. Nikolentzos, M. Thomas, A. Ramirez Rivera, and M. Vazirgiannis, “Image Classification using Graph-based Representations and Graph Neural Networks,” in International Conference Complex Networks and their Applications, 2020.
  2. MVS Silva, L. Bittencourt, and A. Ramirez Rivera, “Towards Federated Learning in Edge Computing for Real-Time Traffic Estimation in Smart Cities,” in Workshop on Urban Computation (CoUrb), 2020.
  3. MTB Iqbal, B. Ryu, A. Ramirez Rivera, F. Makhmudkhujaev, O. Chae, and SH Bae, “Facial Expression Recognition with Active Local Shape Pattern and Learned-Size Block Representations,” IEEE Transactions on Affective Computing, 2020, it hurts: 10.1109/TAFFC.2020.2995432.
  4. R. Quispe, D. Ttito, A. Ramirez Rivera, and H. Pedrini, “Multi-Stream Networks and Ground-Truth Generation for Crowd Counting,” International Journal of Electrical and Computer Engineering Systems, vol. 11, no. 1, pp. 25–33, 2020.
  5. A. Ramirez Rivera, A. Khan, IEI Bekkouch, and T. Sheikh, “Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation,” IEEE Transactions on Neural Networks and Learning Systems, 2020, it hurts: 10.1109 / TNNLS.2020.3027667.

2019

  1. B Kim, A. Ramirez Rivera, O. Chae, and J. Kim, “Background Modeling through Spatiotemporal Edge Feature and Color,” in International Symposium on Visual Computing (ISVC), 2019.
  2. S. Robles, J. Gomez, A. Ramirez Rivera, J. González, N. Padilla, and D. Dujovne, “A Halo Merger Tree Generation and Evaluation Framework,” in Workshop on Theoretical Physics for Deep Learning (ICMLW), 2019.
  3. D. Saire and A. Ramirez Rivera, “Graph Learning Network: A Structure Learning Algorithm,” in Workshop on Learning and Reasoning with Graph-Structured Data (ICMLW), 2019.
  4. D. Ttito, R. Quispe, A. Ramirez Rivera, and H. Pedrini, “Where are the People? A Multi-Stream Convolutional Neural Network for Crowd Counting via Density Map from Complex Images, ”in International Conference on Systems, Signals and Image Processing (IWSSIP), 2019.
  5. A. Khusainova, A. Khan, and A. Ramirez Rivera, “SART — Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings Evaluation,” in International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2019.

2018

  1. P. Zhdanov, A. Khan, A. Ramirez Rivera, and A. Khattak, “Improving Human Action Recognition through Hierarchical Neural Network Classifiers,” in International Joint Conference on Neural Networks (IJCNN), 2018.

2017

  1. J. Arias Figueroa and A. Ramirez Rivera, “Is Simple Better ?: Revisiting Simple Generative Models for Unsupervised Clustering,” in Second workshop on Bayesian Deep Learning (NIPS 2017), 2017.
  2. J. Arias Figueroa and A. Ramirez Rivera, “Learning to Cluster with Auxiliary Tasks: A Semi-Supervised Approach,” in 31th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2017, 2017, pp. 1–8.
  3. A. Dobrenkoi, R. Kuleev, A. Khan, A. Ramirez Rivera, and A. Khattak, “Large Residual Multiple View 3D CNN for False Positive Reduction in Pulmonary Nodule Detection,” in Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE International Conference on, 2017.
  4. M. Gusarev, R. Kuleev, A. Khan, A. Ramirez Rivera, and A. Khattak, “Deep Learning Models for Bone Suppression in Chest Radiographs,” in Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE International Conference on, 2017.
  5. J Kim, A. Ramirez Rivera, B. Kim, K. Roy, and O. Chae, “Background Modeling using Adaptive Properties of Hybrid Features,” in Advanced Video and Signal-Based Surveillance (AVSS), IEEE International Conference on, 2017.
  6. B. Ryu, A. Ramirez Rivera, J. Kim, and O. Chae, “Local Directional Ternary Pattern for Facial Expression Recognition,” IEEE Transactions on Image Processing, vol. 26, no. 12, pp. 6006–6018, 2017, doi: 10.1109/TIP.2017.2726010.

2016

  1. S.Hong, J.Kim, A. Ramirez Rivera, G. Song, and O. Chae, “Edge Shape Pattern for Background Modeling based on Hybrid Local Codes,” in Advanced Video and Signal-Based Surveillance (AVSS), IEEE International Conference on, 2016.

2015

  1. J Kim, A. Ramirez Rivera, B. Ryu, and O. Chae, “Simultaneous foreground detection and classification with hybrid features,” in Computer Vision (ICCV), IEEE International Conference on, 2015, pp. 3307–3315.
  2. A. Ramirez Rivera, J. Rojas Castillo, and O. Chae, “Local Directional Texture Pattern Image Descriptor,” Pattern Recognition Letters, vol. 51, no. 0, pp. 94–100, 2015, doi: 10.1016 / j.patrec.2014.08.012. [Online] . Available at: http://www.sciencedirect.com/science/article/pii/S0167865514002724
  3. A. Ramirez Rivera and O. Chae, “Spatiotemporal Directional Number Transitional Graph for Dynamic Texture Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 10, pp. 2146–2152, 2015, doi: 10.1109 / TPAMI.2015.2392774.

2014

  1. J Kim, A. Ramirez Rivera, B. Ryu, K. Ahn, and O. Chae, “Unattended object based on edge-segment detection distributions,” in Advanced Video and Signal Based Surveillance (AVSS), IEEE International Conference on, 2014, pp. 283-288, it hurts: 10.1109/AVSS.2014.6918682.

2013

  1. J Kim, A. Ramirez Rivera, G. Song, B. Ryu, and O. Chae, “Edge-segment-based Background Modeling: Non-parametric online background update,” in Advanced Video and Signal Based Surveillance (AVSS), IEEE International Conference on, 2013, pp. 214-219, it hurts: 10.1109/AVSS.2013.6636642.
  2. A. Ramirez Rivera, M. Murshed, J. Kim, and O. Chae, “Background Modeling Through Statistical Edge-Segment Distributions,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 8, pp. 1375-1387, Aug. 2013, two: 10.1109/TCSVT.2013.2242551.
  3. J. Kim, M. Murshed, A. Ramirez Rivera, and O. Chae, “Background Modeling Using Edge-Segment Distributions,” International Journal of Advanced Robotic Systems, Feb. 2013, two: 10.5772/54185.
  4. A. Ramirez Rivera, J. Rojas Castillo, and O. Chae, “Local Directional Number Pattern for Face Analysis: Face and Expression Recognition,” IEEE Transactions on Image Processing, vol. 22, no. 5, pp. 1740–1752, 2013, doi: 10.1109/TIP.2012.2235848.

2012

  1. J. Rojas Castillo, A. Ramirez Rivera, and O. Chae, “Robust Facial Recognition Based on Local Gaussian Structural Pattern,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 12, pp. 8399–8413, Dec. 2012
  2. A. Ramirez Rivera, J. Rojas Castillo, and O. Chae, “Local Gaussian Directional Pattern for Face Recognition,” in International Conference on Pattern Recognition (ICPR), 2012, pp. 1000–1003.
  3. A. Ramirez Rivera, J. Rojas Castillo, and O. Chae, “Recognition of Face Expressions Using Local Principal Texture Pattern,” in International Conference on Image Processing (ICIP), 2012, pp. 2609–2612.
  4. J. Rojas Castillo, A. Ramirez Rivera, and O. Chae, “Facial Expression Recognition Based on Local Sign Directional Pattern,” in International Conference on Image Processing (ICIP), 2012, pp. 2613–2616.
  5. A. Ramirez Rivera, B. Ryu, and O. Chae, “Content-Aware Dark Image Enhancement through Channel Division,” IEEE Transactions on Image Processing, vol. 21, no. 9, pp. 3967–3980, Sep. 2012, two: 10.1109/TIP.2012.2198667.
  6. J Kim, A. Ramirez Rivera, M. Park, and O. Chae, “Scene Modeling using Edge Segment Distributions,” in International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), 2012.
  7. M. Murshed, A. Ramirez Rivera, J. Kim, and O. Chae, “Statistical Binary Edge Frequency Accumulation Model for Moving Object Detection,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 7 (B), pp. 4943–4957, Jul. 2012.

2011

  1. A. Ramirez Rivera, M. Murshed, and O. Chae, “Object Detection through Edge Behavior Modeling,” in Advanced Video and Signal-Based Surveillance (AVSS), IEEE International Conference on, 2011, pp. 273–278.
  2. M. Murshed, A. Ramirez Rivera, and O. Chae, “Moving Edge Segment Matching for the Detection of Moving Object,” Reading Notes in Computer Science, vol. 6753, pp. 274–283, Jun. 2011.

2010

  1. M. Murshed, A. Ramirez Rivera, and O. Chae, “Statistical Background Modeling: An Edge Segment Based Moving Object Detection Approach,” in Advanced Video and Signal Based Surveillance (AVSS), IEEE International Conference on, 2010, pp. 300–306.