Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- José María MartínezSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works64 from public data
- The Visual Object Tracking VOT2015 Challenge Results902
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and presents a new VOT 2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute.
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
- 434
- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
- 145
- Semantic-aware scene recognition134
A novel approach for scene recognition based on an end-to-end multi-modal CNN that combines image and context information by means of an attention module is described, which outperforms every other state-of-the-art method while significantly reducing the number of network parameters.
- 79
- The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results66
A significant general improvement of results has been observed, which partly compensate for the more difficult sequences introduced into the dataset in VOT-TIR2016.
- People detection in surveillance: classification and evaluation31
This paper is a postprint of a paper submitted to and accepted for publication in IET Computer Vision and is subject to Institution of Engineering and Technology Copyright.
- Towards automatic waste containers management in cities via computer vision: containers localization and geo-positioning in city maps26
Experimental results indicate that the proposed visual model for waste container detection is able to effectively operate with consistent performance disregarding the container type and the city layout.
- 25
- 24
- On collaborative people detection and tracking in complex scenarios22
The experimental results over an extensive and challenging video dataset point out the state of the art limitations in complex or realistic scenarios and show that the proposed collaborative system significantly reduces these limitations and improves the results in this kind of scenarios.
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives20
A comparison of model performance on the BraTS datasets benchmark is presented and a suitability analysis for top-performing models based on their robustness, efficiency, and completeness of tumor region delineation are introduced.
- 18
- 16
- 15
- 15
- A corpus for benchmarking of people detection algorithms10
A corpus, dataset and associated ground-truth, for the evaluation of people detection algorithms in surveillance video scenarios, along with the design procedure followed to generate it, is described.
- Synthmanticlidar: A Synthetic Dataset For Semantic Segmentation On Lidar Imaging9
This work presents a modified CARLA simulator designed with LiDAR semantic segmentation in mind, with new classes, more consistent object labeling with their counterparts from real datasets such as SemanticKITTI, and the possibility to adjust the object class distribution.
- Exploiting semantic segmentation to boost reinforcement learning in video game environments8
This work proves that using semantic segmentation enables reaching higher performance than the baseline reinforcement learning algorithm without modifying the actual algorithm, and in fewer episodes; it shows noticeable performance improvements when training on multiple levels at the same time; and it allows to apply transfer learning for models trained on visually different environments.
- Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information †8
A coarse-to-fine framework to adapt frame by frame people detectors during runtime classification, without requiring any additional manually labeled ground truth apart from the offline training of the detection model, is proposed.
- 7
- On exploring weakly supervised domain adaptation strategies for semantic segmentation using synthetic data7
This paper proposes a synthetic data generation protocol, which identifies key features affecting performance and provides datasets with variable complexity, and proposes a new curriculum learning strategy to employ several synthetic and real datasets.
- Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on Predicted-View Synthesis6
This work proposes a test-time adaptation approach that leverages the temporal redundancy between images acquired during close proximity operations and establishes a self-supervised learning objective by comparing the synthesized view with the actual one.
- Improved transferability of self-supervised learning models through batch normalization finetuning6
This work proposes a way to obtain a significantly better proxy task by unfreezing and jointly finetuning batch normalization layers together with the classification head of ResNet-50, reaching the state-of-the-art performance on a pathology dataset.
- 5
- Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization †5
A framework to adapt and improve any detector automatically in multi-camera scenarios where people are observed from various viewpoints is proposed and improves the performance of four different state-of-the-art detectors whose Optimal Fixed Thresholds have been determined and fixed during training time using standard datasets.
- Hierarchical detection of persons in groups5
Experimental results show that the proposed hierarchies significantly improve the results with respect to different approaches from the state of the art.
- PDbm: people detection benchmark repository5
A people detection benchmarking repository is presented that includes realistic sequences, people detection ground truth and an evaluation framework that will maintain a comprehensive ranking of submitted methods for years to come.
- Context‐aware part‐based people detection for video monitoring5
Experimental results over various datasets show that the proposed use of context outperforms the related state-of-the-art in part-based people detection in images.
- Post-processing approaches for improving people detection performance5
This work proposes the use of people-background segmentation as a filtering stage in people detection and evaluates the combination of different detection approaches in order to add robustness to the detection and therefore improve the detection results.
- A Multi-configuration Part-based Person Detector5
This paper combines individual body part detectors in a statistical driven way in order to be able to detect persons even in case of failure of any detection of the body parts, i.e., a generic scheme to deal with partial occlusions.
- 5
- GBT ‐ SAM : A Parameter‐Efficient Depth‐Aware Model for Generalizable Brain Tumor Segmentation on mp‐MRI4
GBT‐SAM is presented, a parameter‐efficient deep learning framework that adapts the large‐scale Segment Anything Model (SAM) to volumetric mp‐MRI data and achieves a Dice score of 92.66, the lowest among existing SAM‐based approaches.
- 4
- Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information4
This paper improves the proposed vehicle ReID system by incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step.
- Enhanced people detection combining appearance and motion information4
Experimental results over an extensive dataset show that the proposed combination of two recent approaches based on both information sources significantly improves the results.
- MIRAGE: Retrieval and Generation of Multimodal Images and Texts for Medical Education3
MIRAGE, a multimodal medical text and image retrieval and generation system that allows users to find and generate clinically relevant images from trustworthy sources by mapping both text and images to a shared latent space, enabling semantically meaningful queries, is proposed.
- Transforming urban waste collection inventory: AI-Based container classification and Re-Identification3
The development of the first re-identification system for urban waste elements that uses Artificial Intelligence and Computer Vision, incorporating information from a classification module and geolocation context to enhance post-processing performance is developed.
- Unsupervised Class Generation to Expand Semantic Segmentation Datasets3
An unsupervised pipeline that leverages Stable Diffusion and Segment Anything Module to generate class examples with an associated segmentation mask, and a method to integrate generated cutouts for novel classes in semantic segmentation datasets, all with minimal user input are proposed.
- 3
- 2
- 2
- Instant Customized Summaries Streaming: A Service for Immediate Awareness of New Video Content2
This paper presents the Instant Customized Summaries Streaming service, a multimedia service able to deliver customized video summaries with a minimum delay after the original content has been uploaded to a video repository.
- A Data-Centric Approach to Pedestrian Attribute Recognition: Synthetic Augmentation via Prompt-Driven Diffusion Models1
This paper defines a protocol to identify weakly recognized attributes across multiple datasets and derives a strategy to seamlessly incorporate synthetic samples into training data, which considers prompt-based annotation rules and modifies the loss function.
- 1
- Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances1
A novel synthetic dataset that captures urban scenes under a variety of weather conditions is introduced, providing pixel-perfect, ground-truth-aligned images to facilitate effective feature alignment across domains and proposes a method for domain adaptation and generalization that takes advantage of the multiple versions of each scene.
- An image-processing toolkit for remote photoplethysmography1
The toolkit is a relevant tool for researchers in the remote photoplethysmography field due to their versatility, ease of use, and adaptability and is a relevant tool for researchers in the remote photoplethysmography field due to their versatility, ease of use, and adaptability.
- 1
- 1
- Incorporating wheelchair users in people detection1
A wheelchair users detector is presented to extend people detection, providing a more general solution to detect people in environments such as houses adapted for independent and assisted living, hospitals, healthcare centers and senior residences.
- Performance Evaluation in Video-Surveillance Systems: The EventVideo Project Evaluation Protocols1
This chapter considers the main analysis stages of a typical video-surveillance system (video object segmentation, people detection, video object tracking and event recognition), and introduces their evaluation protocols within the scope of the EventVideo project.
- A Multimodal AI Framework for Medical Education: Integrating Adaptive Image Retrieval, Fast Synthesis, and LLM-Based Clinical Auditing–
An extended version of MIRAGE, a multimodal retrieval and generation system that utilizes a shared latent space to process medical queries by retrieving real images from the ROCO dataset, generating synthetic scans, and providing LLM-based clinical descriptions alongside dual-concept visual comparisons is presented.
- –
- –
- –
- –
- –
- –
- –
- –
- –
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.