Dong Wang

TL;DR: 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.

TL;DR: 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 are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

TL;DR: The background of deep visual tracking is introduced, including the fundamental concepts of visual tracking and related deep learning algorithms, and the existing deep-learning-based trackers are categorize into three classes according to network structure, network function and network training.

TL;DR: A solution which normalizes the word vectors on a hypersphere and constrains the linear transform as an orthogonal transform and can offer better performance on a word similarity task and an English-toSpanish word translation task is proposed.

TL;DR: A novel adaptive spatially-regularized correlation filters model to simultaneously optimize the filter coefficients and the spatial regularization weight is proposed, which could learn an effective spatial weight for a specific object and its appearance variations, and therefore result in more reliable filter coefficients during the tracking process.