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Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things
In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …
from the environment, transmit them to cloud centers, and receive feedback via the Internet …
Deep learning in multi-object detection and tracking: state of the art
Object detection and tracking is one of the most important and challenging branches in
computer vision, and have been widely applied in various fields, such as health-care …
computer vision, and have been widely applied in various fields, such as health-care …
Dropmae: Masked autoencoders with spatial-attention dropout for tracking tasks
In this paper, we study masked autoencoder (MAE) pretraining on videos for matching-
based downstream tasks, including visual object tracking (VOT) and video object …
based downstream tasks, including visual object tracking (VOT) and video object …
Transforming model prediction for tracking
Optimization based tracking methods have been widely successful by integrating a target
model prediction module, providing effective global reasoning by minimizing an objective …
model prediction module, providing effective global reasoning by minimizing an objective …
Transformer meets tracker: Exploiting temporal context for robust visual tracking
In video object tracking, there exist rich temporal contexts among successive frames, which
have been largely overlooked in existing trackers. In this work, we bridge the individual …
have been largely overlooked in existing trackers. In this work, we bridge the individual …
Transformer tracking with cyclic shifting window attention
Transformer architecture has been showing its great strength in visual object tracking, for its
effective attention mechanism. Existing transformer-based approaches adopt the pixel-to …
effective attention mechanism. Existing transformer-based approaches adopt the pixel-to …
TCTrack: Temporal contexts for aerial tracking
Temporal contexts among consecutive frames are far from being fully utilized in existing
visual trackers. In this work, we present TCTrack, a comprehensive framework to fully exploit …
visual trackers. In this work, we present TCTrack, a comprehensive framework to fully exploit …
Learning target candidate association to keep track of what not to track
The presence of objects that are confusingly similar to the tracked target, poses a
fundamental challenge in appearance-based visual tracking. Such distractor objects are …
fundamental challenge in appearance-based visual tracking. Such distractor objects are …
HiFT: Hierarchical feature transformer for aerial tracking
Most existing Siamese-based tracking methods execute the classification and regression of
the target object based on the similarity maps. However, they either employ a single map …
the target object based on the similarity maps. However, they either employ a single map …
Graph attention tracking
Siamese network based trackers formulate the visual tracking task as a similarity matching
problem. Almost all popular Siamese trackers realize the similarity learning via convolutional …
problem. Almost all popular Siamese trackers realize the similarity learning via convolutional …