Robust Visual Tracking via Structured Multi-Task Sparse Learning

By Tianzhu Zhang, Bernard Ghanem, Si Liu, Narendra Ahuja. 2012

427 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Representation & Self-Supervised Learning

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Citations in the corpus

Learning multiple visual domains with residual adapters (2017, methodology): “MTL in DNNs has been applied to various problems ranging from natural language processing [8, 22], speech recognition [14] to computer vision [41, 42, 4].”
12-in-1: Multi-Task Vision and Language Representation Learning (2019, background): “Advances in multi-task learning have been developed in the context of vision [5,20,32,42,52,53], language [10, 25, 26, 31, 37], and robotics [18, 34, 46].”
Cross-Stitch Networks for Multi-task Learning (2016, background): “and their widespread applications in other fields, such as genomics [38], natural language processing [7, 8, 35] and computer vision [3, 10, 30, 31, 40, 51, 53, 58].”
A Survey on Multi-Task Learning (2017, methodology): “In [170], [171], a multi-task dictionary is learned for visual tracking via the `p,1 sparsity.”
Efficient Parametrization of Multi-domain Deep Neural Networks (2018, methodology): “In computer vision, deep MTL models are applied to object tracking [34], facial-landmark detection [35], object and part detection [2], object detection and instance segmentation [10], a collection of low-level and high-level vision tasks [14].”
Facial Landmark Detection by Deep Multi-task Learning (2014, background): “Multi-task learning has proven effective in many computer vision problems [28,29].”
Struck: Structured Output Tracking with Kernels (2016, positive): “The trackers used included MILTrack [3], MTT [46] and ColorPF [47].”
Rotating your face using multi-task deep neural network (2015, background): “Deep learning with multitask learning Recently, many DNN architectures have improved the performance on several computer vision tasks by using multitask learning [21, 23, 4].”
Real-time part-based visual tracking via adaptive correlation filters (2015, methodology): “We compared the proposed algorithm with thirteen state-of-the-art visual trackers: Frag[1], TLD [21], IVT [34], DFT [35], ORIA [44], LOT [32], CXT [13], L1apg [5], MTT [53], ASLA [20], MIL [4], Struck [16] and KCF [17].”
Multi-task Correlation Particle Filter for Robust Object Tracking (2017, methodology): “To solve (3), we use the Accelerated Proximal Gradient method, which has been widely used to efficiently solve convex optimization problems with non-smooth terms [42, 43].”