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Saliency Detection via Graph-Based Manifold Ranking
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 via adaptive structural local sparse appearance model
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
Robust object tracking via sparsity-based collaborative model
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
The Visual Object Tracking VOT2016 Challenge Results
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment. Expand
Learning to Detect Salient Objects with Image-Level Supervision
This paper develops a weakly supervised learning method for saliency detection using image-level tags only, which outperforms unsupervised ones with a large margin, and achieves comparable or even superior performance than fully supervised counterparts. Expand
Deep Mutual Learning
Surprisingly, it is revealed that no prior powerful teacher network is necessary - mutual learning of a collection of simple student networks works, and moreover outperforms distillation from a more powerful yet static teacher. Expand
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
Amulet is presented, a generic aggregating multi-level convolutional feature framework for salient object detection that provides accurate salient object labeling and performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics. Expand
Saliency Detection via Dense and Sparse Reconstruction
A visual saliency detection algorithm from the perspective of reconstruction errors that applies the Bayes formula to integrate saliency measures based on dense and sparse reconstruction errors and refined by an object-biased Gaussian model is proposed. Expand
The Sixth Visual Object Tracking VOT2018 Challenge Results
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many areExpand
Visual Tracking with Fully Convolutional Networks
An in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet shows that the proposed tacker outperforms the state-of-the-art significantly. Expand