Zero-shot temporal activity detection (ZSTAD) is the problem of simultaneous temporal localization and classification of activity segments that are previously unseen during training. This is achieved by transferring the knowledge learned from semantically-related seen activities. This ability to reason about unseen concepts without supervision makes ZSTAD very promising for applications where the acquisition of annotated training videos is difficult. In this paper, we design a transformer-based framework titled TranZAD, which streamlines the detection of unseen activities by casting ZSTAD as a direct set-prediction problem, removing the need for hand-crafted designs and manual post-processing. We show how a semantic information-guided contrastive learning strategy can effectively train TranZAD for the zero-shot setting, enabling the efficient transfer of knowledge from the seen to the unseen activities. To reduce confusion between unseen activities and unrelated background information in videos, we introduce a more efficient method of computing the background class embedding by dynamically adapting it as part of the end-to-end learning. Additionally, unlike existing work on ZSTAD, we do not assume the knowledge of which classes are unseen during training and use the visual and semantic information of only the seen classes for the knowledge transfer. This makes TranZAD more viable for practical scenarios, which we evaluate by conducting extensive experiments on Thumos’14 and Charades.

In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing values. However, the existence of missing values is synonymous with uncertainties not only over the distribution of missing values but also over target class assignments that require careful consideration. In this paper, we propose a simple and effective method for imputing missing features and estimating the distribution of target assignments given incomplete data. In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish. Following this, a predictor network is trained using the imputed samples from the generator network to capture the classification uncertainties and make predictions accordingly. The proposed method is evaluated on CIFAR-10 and MNIST image datasets as well as five real-world tabular classification datasets, under different missingness rates and structures. Our experimental results show the effectiveness of the proposed method in generating imputations as well as providing estimates for the class uncertainties in a classification task when faced with missing values.

Healthcare big data remains under-utilized due to various incompatibility issues between the domains of data analytics and healthcare. The lack of generalizable iterative feature acquisition methods under budget and machine learning models that allow reasoning with a model’s uncertainty are two examples. Meanwhile, a boost to the available data is currently under way with the rapid growth in the Internet of Things applications and personalized healthcare. For the healthcare domain to be able to adopt models that take advantage of this big data, machine learning models should be coupled with more informative, germane feature acquisition methods, consequently adding robustness to the model’s results. We introduce an approach to feature selection that is based on Bayesian learning, allowing us to report the level of uncertainty in the model, combined with false-positive and false-negative rates. In addition, measuring target-specific uncertainty lifts the restriction on feature selection being target agnostic, allowing for feature acquisition based on a target of focus. We show that acquiring features for a specific target is at least as good as deep learning feature selection methods and common linear feature selection approaches for small non-sparse datasets, and surpasses these when faced with real-world data that is larger in scale and sparseness.