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J.R. Serra; J.B. Subirana |
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Adaptive non-cartesian networks for vision. |
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1997 |
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IX International Conference on Image Analysis and Processing. |
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Florence |
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Admin @ si @ SeS1997 |
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212 |
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Damian Sojka; Yuyang Liu; Dipam Goswami; Sebastian Cygert; Bartłomiej Twardowski; Joost van de Weijer |
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Title |
Technical Report for ICCV 2023 Visual Continual Learning Challenge: Continuous Test-time Adaptation for Semantic Segmentation |
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2023 |
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Arxiv |
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The goal of the challenge is to develop a test-time adaptation (TTA) method, which could adapt the model to gradually changing domains in video sequences for semantic segmentation task. It is based on a synthetic driving video dataset – SHIFT. The source model is trained on images taken during daytime in clear weather. Domain changes at test-time are mainly caused by varying weather conditions and times of day. The TTA methods are evaluated in each image sequence (video) separately, meaning the model is reset to the source model state before the next sequence. Images come one by one and a prediction has to be made at the arrival of each frame. Each sequence is composed of 401 images and starts with the source domain, then gradually drifts to a different one (changing weather or time of day) until the middle of the sequence. In the second half of the sequence, the domain gradually shifts back to the source one. Ground truth data is available only for the validation split of the SHIFT dataset, in which there are only six sequences that start and end with the source domain. We conduct an analysis specifically on those sequences. Ground truth data for test split, on which the developed TTA methods are evaluated for leader board ranking, are not publicly available.
The proposed solution secured a 3rd place in a challenge and received an innovation award. Contrary to the solutions that scored better, we did not use any external pretrained models or specialized data augmentations, to keep the solutions as general as possible. We have focused on analyzing the distributional shift and developing a method that could adapt to changing data dynamics and generalize across different scenarios. |
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LAMP |
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Admin @ si @ SLG2023 |
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3993 |
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Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil |
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Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías |
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2014 |
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8th International Congress on University Teaching and Innovation |
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Tarragona; juliol 2014 |
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CIDUI |
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IAM; 600.075;DAG |
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Admin @ si @ SRM2014 |
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2458 |
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Estefania Talavera; Andreea Glavan; Alina Matei; Petia Radeva |
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Title |
Eating Habits Discovery in Egocentric Photo-streams |
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2020 |
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Arxiv |
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CoRR abs/2009.07646
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioural pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals. |
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MILAB |
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Admin @ si @ TGM2020 |
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3536 |
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Francesc Tous |
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Study of Colour Normalisation for Skin Detection. |
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2002 |
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Director: M. Vanrell, Master Thesis. |
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Admin @ si @ Tou2002 |
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325 |
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Estefania Talavera; Nicolai Petkov; Petia Radeva |
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Title |
Towards Unsupervised Familiar Scene Recognition in Egocentric Videos |
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2019 |
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Arxiv |
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CoRR abs/1905.04093
Nowadays, there is an upsurge of interest in using lifelogging devices. Such devices generate huge amounts of image data; consequently, the need for automatic methods for analyzing and summarizing these data is drastically increasing. We present a new method for familiar scene recognition in egocentric videos, based on background pattern detection through automatically configurable COSFIRE filters. We present some experiments over egocentric data acquired with the Narrative Clip. |
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MILAB; no menciona |
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no |
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Admin @ si @ TPR2019b |
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3379 |
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Author |
Estefania Talavera; Petia Radeva; Nicolai Petkov |
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Title |
Towards Emotion Retrieval in Egocentric PhotoStream |
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2019 |
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Arxiv |
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CoRR abs/1905.04107
The availability and use of egocentric data are rapidly increasing due to the growing use of wearable cameras. Our aim is to study the effect (positive, neutral or negative) of egocentric images or events on an observer. Given egocentric photostreams capturing the wearer's days, we propose a method that aims to assign sentiment to events extracted from egocentric photostreams. Such moments can be candidates to retrieve according to their possibility of representing a positive experience for the camera's wearer. The proposed approach obtained a classification accuracy of 75% on the test set, with a deviation of 8%. Our model makes a step forward opening the door to sentiment recognition in egocentric photostreams. |
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MILAB; no proj |
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no |
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Admin @ si @ TRP2019 |
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3381 |
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Author |
George A. Triantafyllid; Nikolaos Thomos; Cristina Cañero; P. Vieyres; Michael G. Strintzis |
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A User Interface for Mobile Robotized Tele-Echography |
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2005 |
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3rd International Conference on Imaging Technologies in Biomedical Sciences (ITBS 2005) |
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Milos Island (Greece) |
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Admin @ si @ TTC2005 |
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587 |
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Chuanming Tang; Kai Wang; Joost van de Weijer; Jianlin Zhang; Yongmei Huang |
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Exploiting Image-Related Inductive Biases in Single-Branch Visual Tracking |
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2023 |
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Arxiv |
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Despite achieving state-of-the-art performance in visual tracking, recent single-branch trackers tend to overlook the weak prior assumptions associated with the Vision Transformer (ViT) encoder and inference pipeline. Moreover, the effectiveness of discriminative trackers remains constrained due to the adoption of the dual-branch pipeline. To tackle the inferior effectiveness of the vanilla ViT, we propose an Adaptive ViT Model Prediction tracker (AViTMP) to bridge the gap between single-branch network and discriminative models. Specifically, in the proposed encoder AViT-Enc, we introduce an adaptor module and joint target state embedding to enrich the dense embedding paradigm based on ViT. Then, we combine AViT-Enc with a dense-fusion decoder and a discriminative target model to predict accurate location. Further, to mitigate the limitations of conventional inference practice, we present a novel inference pipeline called CycleTrack, which bolsters the tracking robustness in the presence of distractors via bidirectional cycle tracking verification. Lastly, we propose a dual-frame update inference strategy that adeptively handles significant challenges in long-term scenarios. In the experiments, we evaluate AViTMP on ten tracking benchmarks for a comprehensive assessment, including LaSOT, LaSOTExtSub, AVisT, etc. The experimental results unequivocally establish that AViTMP attains state-of-the-art performance, especially on long-time tracking and robustness. |
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LAMP |
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Admin @ si @ TWW2023 |
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3978 |
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Author |
Javier Varona |
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Seguimiento visual robusto en entornos complejos, Tesis. |
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2001 |
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Admin @ si @ Var2001 |
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214 |
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Ernest Valveny; Robert Benavente; Agata Lapedriza; Miquel Ferrer; Jaume Garcia; Gemma Sanchez |
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Adaptation of a computer programming course to the EXHE requirements: evaluation five years later |
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2012 |
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European Journal of Engineering Education |
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3 |
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243-254 |
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DAG; CIC; OR; invisible;MV |
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Admin @ si @ VBL2012 |
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2070 |
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Fernando Vilariño |
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Bringing and keeping all the stakeholders together: creating a catalog of models of governance for innovation |
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2017 |
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Open Living Lab Days Report |
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Krakow; August 2017 |
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MV; no menciona;SIAI |
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Admin @ si @ Vil2017b |
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3033 |
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Gabriel Villalonga; Sebastian Ramos; German Ros; David Vazquez; Antonio Lopez |
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3d Pedestrian Detection via Random Forest |
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2014 |
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European Conference on Computer Vision |
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Pedestrian Detection |
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Our demo focuses on showing the extraordinary performance of our novel 3D pedestrian detector along with its simplicity and real-time capabilities. This detector has been designed for autonomous driving applications, but it can also be applied in other scenarios that cover both outdoor and indoor applications.
Our pedestrian detector is based on the combination of a random forest classifier with HOG-LBP features and the inclusion of a preprocessing stage based on 3D scene information in order to precisely determinate the image regions where the detector should search for pedestrians. This approach ends up in a high accurate system that runs real-time as it is required by many computer vision and robotics applications. |
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Zurich; suiza; September 2014 |
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ECCV-Demo |
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ADAS; 600.076 |
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Admin @ si @ VRR2014 |
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2570 |
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Michael Villamizar; A. Sanfeliu; Juan Andrade |
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Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection |
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2006 |
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18th International Conference on Pattern Recognition, 81–85 |
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Hong Kong |
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Admin @ si @ VSA2006a |
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663 |
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Michael Villamizar; A. Sanfeliu; Juan Andrade |
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Orientation Invariant Features for Multiclass Object Recognition |
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2006 |
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11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 655–664 |
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Cancun (Mexico) |
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Series Volume |
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Series Issue |
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Edition |
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ISBN |
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Expedition |
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Conference |
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Notes |
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Approved |
no |
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Call Number |
Admin @ si @ VSA2006b |
Serial |
664 |
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