Public Resources: Content Sets and Software
Content Sets
Software
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Software for experimental evaluation Framework for MOT
This repository is an experimental framework for Multi Object Tracking analysis. Multiple detectors, trackers, and evaluation metrics can be found, and, also, documentation allowing to implement further models in the framework.
Software related to: Juan C. SanMiguel, Jorge Muñoz, and Fabio Poiesi, "Detection-aware multi-object tracking evaluation", 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Madrid, Spain, November 2022, pp. 1-8. (ISBNprint 978-1-6654-6383-6, ISBNelectronic 978-1-6654-6382-9) (DOI 10.1109/AVSS56176.2022.9959412)
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Exploiting Semantic Segmentation To Boost Reinforcement Learning In Video Game Environments
Pytorch implementation of our paper:
Javier Montalvo, Álvaro García-Martín, and Jesus Bescós, “Exploiting Semantic Segmentation to Boost Reinforcement Learning in Video Game Environments”. Multimedia Tools and Applications, 82, 10961–10979, March 2023.
Electronic ISSN 1573-7721, Print ISSN 1380-7501.
DOI 10.1007/s11042-022-13695-1
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VPULab-CityFlow-ReID-Evaluation-Framework
This Evaluation Framework allows researchers to keep on evaluating the performance of their approaches and the ones from the State-of-the-Art over the CitiFlow-ReID dataset. As this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation server during the challenge, which is no more available.
Software related to:
Paula Moral, Álvaro García-Martín, José M. Martínez, and Jesus Bescós, “Enhancing Vehicle Re-Identification Via Synthetic Training Datasets and Re-ranking Based on Video-Clips Information”. Multimedia Tools and Applications, Online 21 March 2023.
Electronic ISSN 1573-7721, Print ISSN 1380-7501.
DOI 10.1007/s11042-023-14511-0
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On exploring weakly supervised domain adaptation strategies for semantic segmentation using synthetic data
Pytorch implementation of our paper:
Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo, and Alvaro Garcia-Martin, “On exploring weakly supervised domain adaptation strategies for semantic segmentation using synthetic data”. Multimedia Tools and Applications, Online 11 March 2023.
Electronic ISSN 1573-7721, Print ISSN 1380-7501.
DOI 10.1007/s11042-023-14662-0
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Spacecraft Pose Estimation: Robust 2D and 3D-Structural Losses and Unsupervised Domain Adaptation by Inter-Model Consensus
Implementation of our paper:
Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Marcos Escudero-Viñolo, "Spacecraft Pose Estimation: Robust 2D and 3D-Structural Losses and Unsupervised Domain Adaptation by Inter-Model Consensus", IEEE Transactions on Aerospace and Electronic Systems, Online 21 August 2023.
Electronic ISSN: 1557-9603 , Print ISSN: 0018-9251.
DOI 10.1109/TAES.2023.3306731
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Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models
Implementation of our paper:
Pablo Marcos-Manchón, Roberto Alcover-Couso, Juan Carlos SanMiguel, José M. Martínez, "Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models", IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle (USA), Jun. 2024. (arxiv available at https://arxiv.org/abs/2403.14291)
Last update 26/03/2024