Over the last few years, Neural Processes have become a useful modelling tool in many application areas, such as healthcare and climate sciences, in which data are scarce and prediction uncertainty estimates are indispensable. However, the current state of the art in the field (AR CNPs; Bruinsma et al., 2023) presents a few issues that prevent its widespread deployment. This work proposes an alternative, diffusion-based approach to NPs which, through conditioning on noised datasets, addresses many of these limitations, whilst also exceeding SOTA performance.

Domain adaptation is essential for activity recognition to ensure accurate and robust performance across diverse environments, sensor types, and data sources. Unsupervised domain adaptation methods have been extensively studied, yet, they require large-scale unlabeled data from the target domain. In this work, we address Few-Shot Domain Adaptation for video-based Activity Recognition (FSDA-AR), which leverages a very small amount of labeled target videos to achieve effective adaptation. This setting is attractive and promising for applications, as it requires recording and labeling only a few, or even a single example per class in the target domain, which often includes activities that are rare yet crucial to recognize. We construct FSDA-AR benchmarks using five established datasets considering diverse domain types: UCF101, HMDB51, EPIC-KITCHEN, Sims4Action, and ToyotaSmartHome. Our results demonstrate that FSDA-AR performs comparably to unsupervised domain adaptation with significantly fewer (yet labeled) target domain samples. We further propose a novel approach, RelaMiX, to better leverage the few labeled target domain samples as knowledge guidance. RelaMiX encompasses a temporal relational attention network with relation dropout, alongside a cross-domain information alignment mechanism. Furthermore, it integrates a mechanism for mixing features within a latent space by using the few-shot target domain samples. The proposed RelaMiX solution achieves state-of-the-art performance on all datasets within the FSDA-AR benchmark. To encourage future research of few-shot domain adaptation for video-based activity recognition, our benchmarks and source code are made publicly available at https://github.com/KPeng9510/RelaMiX.

It's all keen and mean on the artificial intelligence (AI) front in China, which is now vying with the United States as the top dog in the field. US companies can still boast the big cheese operators, but China is making strides in other areas. The UN World Intellectual Property Organisation's Thursday report found that IBM had, with 8,920 patents in the field, the largest AI portfolio, followed by Microsoft with 5,930. China, however, was found dominant in 17 of 20 academic institutions involved in the business of patenting AI. The scramble has been a bitter one.

It's all keen and mean on the artificial intelligence (AI) front in China, which is now vying with the United States as the top dog in the field. US companies can still boast the big cheese operators, but China is making strides in other areas. The UN World Intellectual Property Organisation's Thursday report found that IBM had, with 8,920 patents in the field, the largest AI portfolio, followed by Microsoft with 5,930. China, however, was found dominant in 17 of 20 academic institutions involved in the business of patenting AI. The scramble has been a bitter one.