40 research outputs found
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification
Unmanned aerial vehicles (UAVs) are now widely applied to data acquisition due to its low cost and fast mobility. With the increasing volume of aerial videos, the demand for automatically parsing these videos is surging. To achieve this, current research mainly focuses on extracting a holistic feature with convolutions along both spatial and temporal dimensions. However, these methods are limited by small temporal receptive fields and cannot adequately capture long-term temporal dependencies that are important for describing complicated dynamics. In this article, we propose a novel deep neural network, termed Fusing Temporal relations and Holistic features for aerial video classification (FuTH-Net), to model not only holistic features but also temporal relations for aerial video classification. Furthermore, the holistic features are refined by the multiscale temporal relations in a novel fusion module for yielding more discriminative video representations. More specially, FuTH-Net employs a two-pathway architecture: 1) a holistic representation pathway to learn a general feature of both frame appearances and short-term temporal variations and 2) a temporal relation pathway to capture multiscale temporal relations across arbitrary frames, providing long-term temporal dependencies. Afterward, a novel fusion module is proposed to spatiotemporally integrate the two features learned from the two pathways. Our model is evaluated on two aerial video classification datasets, ERA and Drone-Action, and achieves the state-of-the-art results. This demonstrates its effectiveness and good generalization capacity across different recognition tasks (event classification and human action recognition). To facilitate further research, we release the code at https://gitlab.lrz.de/ai4eo/reasoning/futh-net
Relation Network for Multi-label Aerial Image Classification
Multi-label classification plays a momentous role in perceiving intricate
contents of an aerial image and triggers several related studies over the last
years. However, most of them deploy few efforts in exploiting label relations,
while such dependencies are crucial for making accurate predictions. Although
an LSTM layer can be introduced to modeling such label dependencies in a chain
propagation manner, the efficiency might be questioned when certain labels are
improperly inferred. To address this, we propose a novel aerial image
multi-label classification network, attention-aware label relational reasoning
network. Particularly, our network consists of three elemental modules: 1) a
label-wise feature parcel learning module, 2) an attentional region extraction
module, and 3) a label relational inference module. To be more specific, the
label-wise feature parcel learning module is designed for extracting high-level
label-specific features. The attentional region extraction module aims at
localizing discriminative regions in these features and yielding attentional
label-specific features. The label relational inference module finally predicts
label existences using label relations reasoned from outputs of the previous
module. The proposed network is characterized by its capacities of extracting
discriminative label-wise features in a proposal-free way and reasoning about
label relations naturally and interpretably. In our experiments, we evaluate
the proposed model on the UCM multi-label dataset and a newly produced dataset,
AID multi-label dataset. Quantitative and qualitative results on these two
datasets demonstrate the effectiveness of our model. To facilitate progress in
the multi-label aerial image classification, the AID multi-label dataset will
be made publicly available
Understanding Video Transformers for Segmentation: A Survey of Application and Interpretability
Video segmentation encompasses a wide range of categories of problem
formulation, e.g., object, scene, actor-action and multimodal video
segmentation, for delineating task-specific scene components with pixel-level
masks. Recently, approaches in this research area shifted from concentrating on
ConvNet-based to transformer-based models. In addition, various
interpretability approaches have appeared for transformer models and video
temporal dynamics, motivated by the growing interest in basic scientific
understanding, model diagnostics and societal implications of real-world
deployment. Previous surveys mainly focused on ConvNet models on a subset of
video segmentation tasks or transformers for classification tasks. Moreover,
component-wise discussion of transformer-based video segmentation models has
not yet received due focus. In addition, previous reviews of interpretability
methods focused on transformers for classification, while analysis of video
temporal dynamics modelling capabilities of video models received less
attention. In this survey, we address the above with a thorough discussion of
various categories of video segmentation, a component-wise discussion of the
state-of-the-art transformer-based models, and a review of related
interpretability methods. We first present an introduction to the different
video segmentation task categories, their objectives, specific challenges and
benchmark datasets. Next, we provide a component-wise review of recent
transformer-based models and document the state of the art on different video
segmentation tasks. Subsequently, we discuss post-hoc and ante-hoc
interpretability methods for transformer models and interpretability methods
for understanding the role of the temporal dimension in video models. Finally,
we conclude our discussion with future research directions
Object Tracking Based on Satellite Videos: A Literature Review
Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on WPAFB videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower Difficulty Score (DS) are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-DS group and the Hard-DS group, respectively