Weakly-Supervised Object Detection (WSOD)[1] aims to learn object detectors with only the image-level category labels indicating whether an image contains an object or not. WSOD conducts classification on object proposals (e.g., bounding boxes generated by selective search) with image-level ...
Weakly-supervised action detectionGraph convolutional networksRelational reasoningActor-context relationsThe dominant paradigm in spatiotemporal action detection is to classify actions using spatiotemporal features learned by 2D or 3D Convolutional Networks. We argue that several actions are characterized by ...
unsupervised learning, weakly supervised learning and semi-supervised learning. Supervised learning is the most widely used method across science and industry, whereby each training example has a label: “background” or “signal”. For this challenge, the data do not have labels as we do not kn...
What Goes Where: Predicting Object Distributions from Above In this work, we propose a cross-view learning approach, in which images captured from a ground-level view are used as weakly supervised annotations for in... C Greenwell,S Workman,N Jacobs - IEEE 被引量: 0发表: 2018年 ...
learning curve for beginning to use such, and it was the first step for many researchers who wanted to solve their problems with a Neural network approach [105]. The success of the PINNs can be seen from the rate at which Raissi et al [146] is cited, and the exponentially growing ...
The history of the pharmaceutical profession in Brazil is linked to the arrival of the Portuguese Royal Family in the country, in 1808. During the first three centuries of colonial Brazil, pharmaceutical education was empirically as the university education was prohibited in the domains of the new...
(Khoury et al., 2013). Therefore, it is essential to study and interpret other factors that could influence the model’s decision, such as gender, race, accent, and the characteristics of the microphone used. While some prior research has focused on interpreting the representations in speech ...
Weakly Supervised Object Detection (WSOD) Col- lecting bounding box annotation per object category is al- ready a time-consuming process, which is aggravated even further by fine-grained object detection (e.g., recognising animal species). To overcome this, existing WSOD adopt two scho...
There is a trade-off between annotation time and accuracy: models trained with higher levels of supervision are more accurate than weakly supervised models, but they require costly human-annotated datasets. We propose an intermediate form of supervision, using points, which adds negligible additional ...
Finally, the vast majority of MLonCode problems are unsupervised or at most weakly supervised. It can be very costly to manually label datasets, so researchers typically have to develop correlated heuristics. For example, there are numerous similarity grouping tasks, such as showing similar developer...