[pt] O desenvolvimento de técnicas de aprendizado auto-supervisionado vem ganhando muita visibilidade na área de Visão Computacional pois possibilita o pré-treinamento de redes neurais profundas sem a necessidade de dados anotados. Em alguns domínios, as anotações são custosas, pois demandam muito trabalho especializado para a rotulação dos dados. Esse problema é muito comum no setor de Óleo e Gás, onde existe um vasto volume de dados não interpretados. O presente trabalho visa aplicar a técnica de aprendizado auto-supervisionado denominada Masked Autoencoders para pré-treinar modelos Vision Transformers com dados sísmicos. Para avaliar o pré-treino, foi aplicada a técnica de transfer learning para o problema de segmentação de fácies sísmicas. Na fase de pré-treinamento foram empregados quatro volumes sísmicos distintos. Já para a segmentação foi utilizado o dataset Facies-Mark e escolhido o modelo da literatura Segmentation Transformers. Para avaliação e comparação da performance da metodologia foram empregadas as métricas de segmentação utilizadas pelo trabalho de benchmarking de ALAUDAH (2019). As métricas obtidas no presente trabalho mostraram um resultado superior. Para a métrica frequency weighted intersection over union, por exemplo, obtivemos um ganho de 7.45 por cento em relação ao trabalho de referência. Os resultados indicam que a metodologia é promissora para melhorias de problemas de visão computacional em dados sísmicos. / [en] The development of self-supervised learning techniques has gained a lot of visibility in the field of Computer Vision as it allows the pre-training of deep neural networks without the need for annotated data. In some domains, annotations are costly, as they require a lot of specialized work to label the data. This problem is very common in the Oil and Gas sector, where there is a vast amount of uninterpreted data. The present work aims to apply the self-supervised learning technique called Masked Autoencoders to pre-train Vision Transformers models with seismic data. To evaluate the pre-training, transfer learning was applied to the seismic facies segmentation problem. In the pre-training phase, four different seismic volumes were used. For the segmentation, the Facies-Mark dataset was used and the Segmentation Transformers model was chosen from the literature. To evaluate and compare the performance of the methodology, the segmentation metrics used by the benchmarking work of ALAUDAH (2019) were used. The metrics obtained in the present work showed a superior result. For the frequency weighted intersection over union (FWIU) metric, for example, we obtained a gain of 7.45 percent in relation to the reference work. The results indicate that the methodology is promising for improving computer vision problems in seismic data.

Today the process of sorting second-hand clothes and textiles is mostly manual. In this master’s thesis, methods for automating this process as well as improving the manual sorting process have been investigated. The methods explored include the automatic prediction of price and intended usage for second-hand clothes, as well as different types of image retrieval to aid manual sorting. Two models were examined: CLIP, a multi-modal model, and MAE, a self-supervised model. Quantitatively, the results favored CLIP, which outperformed MAE in both image retrieval and prediction. However, MAE may still be useful for some applications in terms of image retrieval as it returns items that look similar, even if they do not necessarily have the same attributes. In contrast, CLIP is better at accurately retrieving garments with as many matching attributes as possible. For price prediction, the best model was CLIP. When fine-tuned on the dataset used, CLIP achieved an F1-Score of 38.08 using three different price categories in the dataset. For predicting the intended usage (either reusing the garment or exporting it to another country) the best model managed to achieve an F1-Score of 59.04.

Football trajectory modeling is a powerful tool for predicting and evaluating the movement of a football and its dynamics. Masked autoencoders are scalable self-supervised learners used for representation learning of partially observable data. Masked autoencoders have been shown to provide successful results in pre-training for computer vision and natural language processing tasks. Using masked autoencoders in the multivariate time-series data field has not been researched to the same extent. This thesis aims to investigate the potential of using masked autoencoders for multivariate time-series modeling for football trajectory data in collaboration with Tracab. Two versions of the masked autoencoder network with alterations are tested, which are implemented to be used with multivariate time-series data. The resulting models are used to detect anomalies in the football trajectory and propose corrections based on the reconstruction. The results are evaluated, discussed, and compared against the tracked and manually corrected value of the ball trajectory. The performance of the different frameworks is compared and the overall anomaly detection capabilities are discussed. The result suggested that even though the regular autoencoder version had a smaller average reconstruction error during training and testing, using masked autoencoders improved the anomaly detection performance. The result suggested that neither the regular autoencoder nor the masked autoencoder managed to propose plausible trajectories to correct anomalies in the data. This thesis promotes further research to be done in the field of using masked autoencoders for time series and trajectory modeling. / Modellering av en fotbolls bollbana är ett kraftfullt verktyg för att förutse och utvärdera rörelsen och dynamiken hos en fotboll. Maskerade autoencoders är skalbara självövervakande inlärare som används för representationsinlärning av delvis synlig data. Maskerade autoencoders har visat sig ge framgångsrika resultat vid förträning inom datorseende och naturlig språkbearbetning. Användningen av maskerade autoencoders för multivariat tidsserie-data har det inte forskats om i samma omfattning. Syftet med detta examensarbete är att undersöka potentialen för maskerade autoencoders inom tidsseriemodellering av bollbanor för fotboll i samarbete med Tracab. Två versioner av maskerade autoencoders anpassade för tidsserier testas. De tränade modellerna används för att upptäcka avvikelser i detekterade fotbollsbanor och föreslå korrigeringar baserat på rekonstruktionen. Resultaten utvärderas, diskuteras och jämförs med det detekterade och manuellt korrigerade värdet för fotbollens bollbana. De olika ramverken jämförs och deras förmåga för detektion och korrigering av avvikelser diskuteras. Resultatet visade att även om den vanliga autoencoder-versionen hade ett mindre genomsnittligt rekonstruktionsfel efter träning, så bidrog användningen av maskerade autoencoders till en förbättring inom detektering av avvikelser. Resultatet visade att varken den vanliga autoencodern eller den maskerade autoencodern lyckades föreslå trovärdiga bollbanor för att korrigera de funna avvikelserna i datan. Detta examensarbete främjar ytterligare forskning inom användningen av maskerade autoencoders för tidsserier och banmodellering.