Fat is key for fudgy brownies. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. Astronauts aboard the International Space Station have brownies on their menu too . But what makes a perfect brownie?

Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person action recognition is an under-explored problem, with lack of datasets and limited consideration of first-person video characteristics. This paper focuses on addressing this problem. Firstly, we propose two small-scale first-person video domain adaptation datasets: ADL$_{small}$ and GTEA-KITCHEN. Secondly, we introduce channel-temporal attention blocks to capture the channel-wise and temporal-wise relationships and model their inter-dependencies important to first-person vision. Finally, we propose a Channel-Temporal Attention Network (CTAN) to integrate these blocks into existing architectures. CTAN outperforms baselines on the two proposed datasets and one existing dataset EPIC$_{cvpr20}$.

Most of us love a good chocolate brownie. The existence of chocolate is not the only reason. We also love a good brownie because of its texture, convenient serving size, the presence of nuts (or other confections), and its "just right" density (otherwise, it may be too fluffy like a chocolate cake; or it may be too heavy like a dense fudge). We therefore love the chocolate brownie because it has several delightful, distinguishing, and delicious features. Selecting good features in our data collection similarly delights us in many ways.