Benchmarking Domain Adaptation Methods on Aerial Datasets.

Deep learning grew in importance in recent years due to its versatility and excellent performance on supervised classification tasks. A core assumption for such supervised approaches is that the training and testing data are drawn from the same underlying data distribution. This may not always be the case, and in such cases, the performance of the model is degraded. Domain adaptation aims to overcome the domain shift between the source domain used for training and the target domain data used for testing. Unsupervised domain adaptation deals with situations where the network is trained on labeled data from the source domain and unlabeled data from the target domain with the goal of performing well on the target domain data at the time of deployment. In this study, we overview seven state-of-the-art unsupervised domain adaptation models based on deep learning and benchmark their performance on three new domain adaptation datasets created from publicly available aerial datasets. We believe this is the first study on benchmarking domain adaptation methods for aerial data. In addition to reporting classification performance for the different domain adaptation models, we present t-SNE visualizations that illustrate the benefits of the adaptation process.

Benchmarking Deep Trackers on Aerial Videos.

In recent years, deep learning-based visual object trackers have achieved state-of-the-art performance on several visual object tracking benchmarks. However, most tracking benchmarks are focused on ground level videos, whereas aerial tracking presents a new set of challenges. In this paper, we compare ten trackers based on deep learning techniques on four aerial datasets. We choose top performing trackers utilizing different approaches, specifically tracking by detection, discriminative correlation filters, Siamese networks and reinforcement learning. In our experiments, we use a subset of OTB2015 dataset with aerial style videos; the UAV123 dataset without synthetic sequences; the UAV20L dataset, which contains 20 long sequences; and DTB70 dataset as our benchmark datasets. We compare the advantages and disadvantages of different trackers in different tracking situations encountered in aerial data. Our findings indicate that the trackers perform significantly worse in aerial datasets compared to standard ground level videos. We attribute this effect to smaller target size, camera motion, significant camera rotation with respect to the target, out of view movement, and clutter in the form of occlusions or similar looking distractors near tracked object.

Rigidity of lamellar nanosheets.

Lamellar nanosheets of contrasting materials are ubiquitous in functional coatings and electronic devices. They also represent a unique paradigm for polymer nanocomposites. Here, we use fluid-assembled lamellar nanosheets - alternating layers of polymer and single-wall carbon nanotubes (SWCNTs) - to gain insight into the flexural mechanics of such hybrid films. Specifically, we measure the modulus and yield strain as a function of both layer thickness and the total number of layers. Overall, we find that the multi-layered films exhibit the greatest synergistic effects near a layer thickness of 20 nm or less, which we relate to the characteristic width of the SWCNT-polymer interface. For all layer thicknesses, we find that the nanosheets have realized the bulk limit by six layers. Our results have potentially profound implications for controlling the rigidity and durability of polymer nanocomposites, thin hybrid films and flexible heterojunctions.