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Deep reinforcement learning in computer vision: a comprehensive survey
Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …
the powerful representation of deep neural networks. Recent works have demonstrated the …
Deep learning for visual tracking: A comprehensive survey
Visual target tracking is one of the most sought-after yet challenging research topics in
computer vision. Given the ill-posed nature of the problem and its popularity in a broad …
computer vision. Given the ill-posed nature of the problem and its popularity in a broad …
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 …
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 …
Siamese box adaptive network for visual tracking
Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-
defined anchor boxes to accurately estimate the scale and aspect ratio of a target …
defined anchor boxes to accurately estimate the scale and aspect ratio of a target …
Probabilistic regression for visual tracking
Visual tracking is fundamentally the problem of regressing the state of the target in each
video frame. While significant progress has been achieved, trackers are still prone to failures …
video frame. While significant progress has been achieved, trackers are still prone to failures …
SiamCAR: Siamese fully convolutional classification and regression for visual tracking
By decomposing the visual tracking task into two subproblems as classification for pixel
category and regression for object bounding box at this pixel, we propose a novel fully …
category and regression for object bounding box at this pixel, we propose a novel fully …
Learning discriminative model prediction for tracking
The current strive towards end-to-end trainable computer vision systems imposes major
challenges for the task of visual tracking. In contrast to most other vision problems, tracking …
challenges for the task of visual tracking. In contrast to most other vision problems, tracking …
Siamrpn++: Evolution of siamese visual tracking with very deep networks
Siamese network based trackers formulate tracking as convolutional feature cross-
correlation between target template and searching region. However, Siamese trackers still …
correlation between target template and searching region. However, Siamese trackers still …
AutoTrack: Towards high-performance visual tracking for UAV with automatic spatio-temporal regularization
Most existing trackers based on discriminative correlation filters (DCF) try to introduce
predefined regularization term to improve the learning of target objects, eg, by suppressing …
predefined regularization term to improve the learning of target objects, eg, by suppressing …