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Object Tracking Benchmark
TLDR
An extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria is carried out to identify effective approaches for robust tracking and provide potential future research directions in this field. Expand
Incremental Learning for Robust Visual Tracking
TLDR
A tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target, and includes a method for correctly updating the sample mean and a forgetting factor to ensure less modeling power is expended fitting older observations. Expand
Saliency Detection via Graph-Based Manifold Ranking
TLDR
This work considers both foreground and background cues in a different way and ranks the similarity of the image elements with foreground cues or background cues via graph-based manifold ranking, defined based on their relevances to the given seeds or queries. Expand
Visual tracking with online Multiple Instance Learning
TLDR
It is shown that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. Expand
Robust Object Tracking with Online Multiple Instance Learning
TLDR
It is shown that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks. Expand
Hierarchical Convolutional Features for Visual Tracking
TLDR
This paper adaptively learn correlation filters on each convolutional layer to encode the target appearance and hierarchically infer the maximum response of each layer to locate targets. Expand
Visual tracking via adaptive structural local sparse appearance model
TLDR
A simple yet robust tracking method based on the structural local sparse appearance model which exploits both partial information and spatial information of the target based on a novel alignment-pooling method and employs a template update strategy which combines incremental subspace learning and sparse representation. Expand
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
TLDR
This paper proposes the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images and generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Expand
Robust object tracking via sparsity-based collaborative model
TLDR
A robust appearance model that exploits both holistic templates and local representations is proposed and the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively and alleviate the drift problem. Expand
Learning to Adapt Structured Output Space for Semantic Segmentation
TLDR
A multi-level adversarial network is constructed to effectively perform output space domain adaptation at different feature levels and it is shown that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and visual quality. Expand
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