VIA MADRID

 

Research  

 

GTI Data   

 

Open databases created and software developed by the GTI and supplemental material to papers.  

 

Databases  

SportCLIP (2025): Multi-sport dataset for text-guided video summarization.
Ficosa (2024): The FNTVD dataset has been generated using the Ficosa's recording car.
MATDAT (2023):  More than 90K labeled images of martial arts tricking.
SEAW – DATASET (2022): 3 stereoscopic contents in 4K resolution at 30 fps.
UPM-GTI-Face dataset (2022): 11 different subjects captured in 4K, under 2 scenarios, and 2 face mask conditions.
LaSoDa (2022): 60 annotated images from soccer matches in five stadiums with different characteristics and light conditions.
PIROPO Database (2021):People in Indoor ROoms with Perspective and Omnidirectional cameras.
EVENT-CLASS (2021): High-quality 360-degree videos in the context of tele-education.
Parking Lot Occupancy Database (2020)
Nighttime Vehicle Detection database (NVD) (2019)
Hand gesture dataset (2019): Multi-modal Leap Motion dataset for Hand Gesture Recognition.
ViCoCoS-3D (2016): VideoConference Common Scenes in 3D.
LASIESTA database (2016): More than 20 sequences to test moving object detection and tracking algorithms.
Hand gesture database (2015): Hand-gesture database composed by high-resolution color images acquired with the Senz3D sensor.
HRRFaceD database (2014):Face database composed by high resolution images acquired with Microsoft Kinect 2 (second generation).
Lab database (2012): Set of 6 sequences to test moving object detection strategies.
Vehicle image database (2012): More than 7000 images of vehicles and roads.           

 

Software  

NaviFormer (2025): A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem.
Empowering Computer Vision in Higher Education(2024): A Novel Tool for Enhancing Video Coding Comprehension.
Engaging students in audiovisual coding through interactive MATLAB GUIs (2024)
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem (2023)
Solving Routing Problems for Multiple Cooperative Unmanned Aerial Vehicles using Transformer Networks (2023)
Vision Transformers and Traditional Convolutional Neural Networks for Face Recognition Tasks (2023)
Faster GSAC-DNN (2023): A Deep Learning Approach to Nighttime Vehicle Detection Using a Fast Grid of Spatial Aware Classifiers.
SETForSeQ (2020): Subjective Evaluation Tool for Foreground Segmentation Quality. 
SMV Player for Oculus Rift (2016)
Bag-D3P (2016): Face recognition using depth information. 
TSLAB (2015): Tool for Semiautomatic LABeling.    

   

Supplementary material  

Viewpoint-Invariant Soccer Pitch Registration Using Geometric and Learned Features (2025)
Soccer line mark segmentation and classification with stochastic watershed transform (2022)
A fully automatic method for segmentation of soccer playing fields (2022)
Grass band detection in soccer images for improved image registration (2022)
Evaluating the Influence of the HMD, Usability, and Fatigue in 360VR Video Quality Assessments (2020)
Automatic soccer field of play registration (2020)   
Augmented reality tool for the situational awareness improvement of UAV operators (2017)
Detection of static moving objects using multiple nonparametric background-foreground models on a Finite State Machine (2015)
Real-time nonparametric background subtraction with tracking-based foreground update (2015)  
Camera localization using trajectories and maps (2014)

 

                                                                                                                                                                                                                             
 
                                                                   
 
                                                                                                                                                             
 
      

 

 

VIA MADRID

Our colleague César Díaz participated as a speaker at the VIA Madrid Summer School, an initiative focused on computer vision and artificial intelligence aimed at master's and PhD students.

In his talk titled "Smarter pixel squeezing: reshaping image and video compression with AI", he explained how artificial intelligence is revolutionizing image and video compression through the combination of autoencoders and generative models. He also addressed current challenges faced by this technology, such as high computational demands, the lack of universal standards, or the appearance of undesired artifacts, as well as research directions aimed at overcoming these limitations.

 

VIAMAdrid Csic cesar

 

In addition, Enmin Zhong and Marcos de Rodrigo, researchers from the group, presented the talk "Teaching Foundation Models to See Movement", in which they showed how to overcome the limitations of foundation models like CLIP, which are originally trained only on static images. Using their work ViMoCLIP as an example, they described a teacher–student distillation strategy to efficiently integrate motion information (optical flow). The resulting model, ViMoCLIP, achieves an improvement of approximately 2.5 points in fine-grained recognition tasks, without relying on text prompts or heavy 3D backbones—demonstrating the potential of multimodality (RGB + flow) to enhance temporal understanding at low computational cost.

 

VIAMAdrid Csic enmin

 

The summer school was organized by CSIC, Universidad Autónoma de Madrid (UAM), Universidad Carlos III de Madrid (UC3M), and Universidad Politécnica de Madrid (UPM), within the framework of the IDEALCV-CM project, funded by the Comunidad de Madrid. This project aims to advance the development of deep learning-based computer vision systems, improving their accuracy, robustness, efficiency, and explainability.