Eines per al desenvolupament de competències d'enginyeria en un assignatura d'intel·ligència artificial

En aquesta comunicació recollim l'experiència de tres anys en l'adaptació al nou model educatiu en una assignatura de 4rt d'enginyeria informàtica. L'objectiu ha estat el d'aconseguir una metodologia que emfatitzi les competències dels enginyers en una assignatura orientada a la resolució de problemes com és la Intel·ligència Artificial. Les competències sobre les que hem incidit més són: la capacitat d'anàlisi de problemes contextualitzats, la generalització dels problemes d'acord amb els coneixement adquirits prèviament, la capacitat de plantejar solucions viables, l'avaluació de les solucions i l'anàlisi del seu rendiment. Complementàriament, això s'acompanya del treball en competències transversals com ara el treball en equip i la defensa en públic de les solucions, i l'anglès. Per dur-ho a terme a les classes de teoria es treballa totalment orientat a la resolució de casos i s'han dissenyat noves sessions de problemes i pràctiques. En aquestes sessions s'han augmentat el nombre de grups per a permetre un seguiment real dels alumnes. Els problemes estan pensats per a potenciar la capacitat d'anàlisi amb enunciats de difícil o, fins i tot, de solució desconeguda, per a potenciar l'esperit crític i la participació activa a classe. La defensa pública de la feina es duu a terme, després d'una classe teòrica sobre la matèria, en les sessions de practiques,on es simula un projecte real de principi a fi. La feina en equip es potencia tan en problemes com en practiques amb grups de 2 a 5 alumnes. L'ús de la llengua anglesa s'ha introduït exitosament el darrer any, tant en els enunciats, en les sessions de problemes, així com a les presentacions dels estudiants.In this paper we review a 3 years experience on the adaptation of a subject to the new education model. We are working on the Artificial Intelligence subject that is hold during the fourth year of the Computer Science degree. The general goal of this adaptation has been to reach a new methodology that focuses on general engineering competences in the framework of a subject that is completely problem-based, this is Artificial Intelligence. The main competences we have focused the more are: the analysis of problems in a real context, the generalisation of solutions according to previously learnt techniques, the ability to plan feasible solutions, the evaluation of proposed solutions and the analysis of their performance. Complementary, we add the work on transversal competences such as, working cooperatively in a team, presenting and defending the proposed solutions and English speaking. To reach these goals we propose to work in a completely problem-based methodology in the theoretical part of the subject. The part of the subject devoted to solve problems is dealt by working in seminars with a reduced number of students that work in an interactive way. Finally, the practical part of the subject is oriented to simulate the development of a real project, that is solved cooperatively in groups of 4 or 5 students that meet in different sessions. The students design and implement a new solution to a problem statement; in the end they defend their own solution in a viva in front of their colleagues. The use of the English language is inserted in different points of the course

Real-time tracker with fast recovery from target loss

In this paper, we introduce a variation of a state-of-the-art real-time tracker (CFNet), which adds to the original algorithm robustness to target loss without a significant computational overhead. The new method is based on the assumption that the feature map can be used to estimate the tracking confidence more accurately. When the confidence is low, we avoid updating the object's position through the feature map; instead, the tracker passes to a single-frame failure mode, during which the patch's low-level visual content is used to swiftly update the object's position, before recovering from the target loss in the next frame. The experimental evidence provided by evaluating the method on several tracking datasets validates both the theoretical assumption that the feature map is associated to tracking confidence, and that the proposed implementation can achieve target recovery in multiple scenarios, without compromising the real-time performance.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0784

Data Augmentation through Pseudolabels in Automatic Region Based Coronary Artery Segmentation for Disease Diagnosis

Coronary Artery Diseases(CADs) though preventable are one of the leading causes of death and disability. Diagnosis of these diseases is often difficult and resource intensive. Segmentation of arteries in angiographic images has evolved as a tool for assistance, helping clinicians in making accurate diagnosis. However, due to the limited amount of data and the difficulty in curating a dataset, the task of segmentation has proven challenging. In this study, we introduce the idea of using pseudolabels as a data augmentation technique to improve the performance of the baseline Yolo model. This method increases the F1 score of the baseline by 9% in the validation dataset and by 3% in the test dataset.Comment: arXiv admin note: text overlap with arXiv:2310.0474

Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation

We present our novel deep multi-task learning method for medical image segmentation. Existing multi-task methods demand ground truth annotations for both the primary and auxiliary tasks. Contrary to it, we propose to generate the pseudo-labels of an auxiliary task in an unsupervised manner. To generate the pseudo-labels, we leverage Histogram of Oriented Gradients (HOGs), one of the most widely used and powerful hand-crafted features for detection. Together with the ground truth semantic segmentation masks for the primary task and pseudo-labels for the auxiliary task, we learn the parameters of the deep network to minimise the loss of both the primary task and the auxiliary task jointly. We employed our method on two powerful and widely used semantic segmentation networks: UNet and U2Net to train in a multi-task setup. To validate our hypothesis, we performed experiments on two different medical image segmentation data sets. From the extensive quantitative and qualitative results, we observe that our method consistently improves the performance compared to the counter-part method. Moreover, our method is the winner of FetReg Endovis Sub-challenge on Semantic Segmentation organised in conjunction with MICCAI 2021