Oriented Object Detection (OOD) has received increased attention in the past years, being a suitable solution for detecting elongated objects in remote sensing analysis. In particular, using regression loss functions based on Gaussian distributions has become attractive since they yield simple and differentiable terms. However, existing solutions are still based on regression heads that produce Oriented Bounding Boxes (OBBs), and the known problem of angular boundary discontinuity persists. In this work, we propose a regression head for OOD that directly produces Gaussian distributions based on the Cholesky matrix decomposition. The proposed head, named GauCho, theoretically mitigates the boundary discontinuity problem and is fully compatible with recent Gaussian-based regression loss functions. Furthermore, we advocate using Oriented Ellipses (OEs) to represent oriented objects, which relates to GauCho through a bijective function and alleviates the encoding ambiguity problem for circular objects. Our experimental results show that GauCho can be a viable alternative to the traditional OBB head, achieving results comparable to or better than state-of-the-art detectors for the challenging dataset DOTA. Our code will be available at https://github.com/jhlmarques/GauCho.

Supervised object detection requires annotated datasets for training and evaluation purposes. However, human annotation of large datasets is error-prone, and frequent mistakes are erroneous labels, missing objects, and impre-cise bounding boxes. The main goals of this work are to quantify the extent of annotation noise in terms of corner-wise discrepancies, assess how it impacts evaluation met-rics for object detection, and propose noise-aware alter-natives that serve as upper and lower bounds for a base-line metric. We focus our analysis on the Microsoft COCO dataset and re-evaluate several state-of-the-art object de-tectors using the proposed metrics. We show that the Average Precision (AP) metric might be considerably over or under-estimated, particularly for small objects and restrictive IoU acceptance thresholds. Our code is available at https://github.com/Artcsl/Error-Aware.

Oriented object detection is a challenging and relatively new problem. Most existing approaches are based on deep learning and explore Oriented Bounding Boxes (OBBs) to represent the objects. They are typically based on adaptations of traditional detectors that work with Horizontal Bounding Boxes (HBBs), which have been exploring IoU-like loss functions to regress the HBBs. However, extending this idea for OBBs is challenging due to complex formulations or requirement for customized backpropagation implementations. Furthermore, using OBBs presents limitations for irregular or roughly circular objects, since the definition of the ideal OBB is an ambiguous and ill-posed problem. In this work, we jointly tackle the problem of training, representing, and evaluating oriented detectors. We explore Gaussian distributions-called Gaussian Bounding Boxes (GBBs)-as fuzzy representations for oriented objects and propose using a similarity metric between two GBBs based on the Hellinger distance. We show that this metric leads to a differentiable closed-form expression that can be directly used as a localization loss term to train OBB object detectors. We also show that GBBs present a natural representation as elliptical regions (called EBBs), which inherently mitigate ambiguity representation for circular objects. Finally, we empirically show that the proposed similarity metric computed between two GBBs strongly correlates with the IoU between the corresponding EBBs, motivating the name Probabilistic Intersection-over-Union (ProbIoU). Our experiments show that results using ProbIoU as a regression loss are competitive with state-of-the-art alternatives without requiring additional hyperparameters or customized implementations, and that ProbIoU is a promising alternative to evaluate oriented object detectors. Our code is available at https://github.com/ProbIOU/.

We propose an approach for comparing curricula of study programs in higher education.Pre-trained word embeddings are fine-tuned in a study program classification task, where each curriculum is represented by the names and content of its courses.By combining metric learning with a novel course-guided attention mechanism, our method obtains more accurate curriculum representations than strong baselines.Experiments on a new dataset with curricula of computing programs demonstrate the intuitive power of our approach via attention weights, topic modeling, and embeddings visualizations.We also present a use case comparing computing curricula from USA and Latin America to showcase the capabilities of our improved embeddings representations.