UvA-DARE (Digital Academic Repository) Siamese Instance Search for Tracking

By Ran Tao, Efstratios Gavves, Arnold M. W. Smeulders. 2016

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Citations in the corpus

Transporter Networks: Rearranging the Visual World for Robotic Manipulation (2020, methodology): “This operation is analogous to the search function in visual tracking literature [35, 36, 37] where ot[Tpick] is the exemplar and ot is the search area, or in retrieval systems literature where ot[Tpick] is the query and ot is the key.”
Deeper and Wider Siamese Networks for Real-Time Visual Tracking (2019, methodology): “The pioneering works are SINT [34] and SiamFC [2], which each employ a Siamese network to offline train a similarity metric between the object target and candidate image patches.”
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking (2017, background): “Recent state-of-the-art trackers can be generally divided into two categories, including correlation filter (CF) trackers [1, 14, 8, 24, 10] and deep trackers [27, 2, 35, 32].”
Multi-task Correlation Particle Filter for Robust Object Tracking (2017, methodology): “We evaluate our MCPF algorithm with 29 trackers in [36] and other 22 state-of-the-art trackers using the source codes including MEEM [39], TGPR [12], KCF [16], RPT [21], MUSTer [18], DSST [9], LCT [26], CF2 [25], SCF [23], HDT [29], Staple [2], SRDCF [10], DeepSRDCF [7], SRDCFdecon [8], CNN-SVM [17]”
ATOM: Accurate Tracking by Overlap Maximization (2018, methodology): “Our tracker outperforms MDNet with relative gains of 14% and STRCF SINT ECO DSiam StructSiam SiamFC VITAL MDNet DaSiam- ATOM [19] [34] [4] [10] [41] [2] [32] [26] RPN[42]”
Learning Dynamic Memory Networks for Object Tracking (2018, methodology): “To further show our tracking accuracy, we also compared with another 8 recent state-of-the art trackers that are not real-time speed, including CREST [26], CSR-DCF [19], MCPF [38], SRDCFdecon [9], SINT [29], SRDCF [6], HDT [23], HCF [20] on OTB-2015.”
A Twofold Siamese Network for Real-Time Object Tracking (2018, background): “There are a large number of follow-up work[39, 38, 15, 32, 11] of SiamFC.”
Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (2017, methodology): “Recently proposed methods [31, 24, 12] overcome the gap by training their network with a large amount of tracking video datasets [40, 20, 29].”
Learning Background-Aware Correlation Filters for Visual Tracking (2017, methodology): “The excellent performance of deep convolutional neural networks (CNNs) on several challenging vision tasks [19, 31, 24, 14] has encouraged more recent works to either exploit CNN deep features within CF framework [26, 9, 11] or design deep architectures [2, 28, 34, 5, 32, 33] for robust visual track”
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking (2018, methodology): “As a contrast, the Siamese Tracker [19,44] learns a function f(z, z) to compare an exemplar image z to a candidate image z of the same size.”
Hard-Aware Deeply Cascaded Embedding (2016, background): “Deep metric embedding has attracted increasing attention for various tasks , such as visual product search [6, 18, 23, 27, 35], face recognition [21, 32, 41, 4], local image descriptor learning [9, 2, 12, 24], person/vehicle reidentification [22, 7, 43, 17], zero-shot image classification [19, 45, 5”
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking (2021, background): “In recent years, the Siamese network has gained significant popularity, which deals with the tracking task by template matching [1, 44, 19].”
Scalable Object-Oriented Sequential Generative Models (2019, unknown): “Overall, there has numerous amount of works on object tracking from the computer vision community using the same “bounding box”-representation approach proposed in SCALOR (Kosiorek et al., 2017; Ning et al., 2017; Nam & Han, 2016; Tao et al., 2016), but such methods use provided labels to tackles th”
Ocean: Object-aware Anchor-free Tracking (2020, methodology): “, SINT [35] and SiamFC [1], employ Siamese networks to learn a similarity metric between the object target and candidate image patches, thus modeling the tracking as a search problem of the target over the entire image.”
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark (2020, background): “To alleviate this problem, deep Siamese networks have been introduced for object tracking [6, 75].”
Learning Dynamic Siamese Network for Visual Object Tracking (2017, background): “As a result, the DSiam and DSiamM architecture truly allows to train a better online updatable tracker instead of just finding a good matching function [26, 2].”
VITAL: VIsual Tracking via Adversarial Learning (2018, methodology): “We compare VITAL with 29 trackers from the OTB-2013 benchmark [59] and other 28 stateof-the-art trackers including DSST [10], KCF [22], TGPR [16], MEEM [63], RPT [34], LCT [37], MUSTer [25], HCFT [36], FCNT [57], SRDCF [12], CNN-SVM [24], DeepSRDCF [11], DAT [41], Staple [3], SRDCFdecon [13], CCOT [”
Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking (2019, methodology): “The results are compared with 20 state-of-the-art trackers with both hand-crafted based trackers and deep-based trackers, i.e., KCF [13], DSST [8], SAMF [18], MUSTER [14], BACF [15], SRDCF [7], STAPLE CA [21], MCCT-H [27], STRCF [16], ECO-HC (with gray-scale) [5], ECO [5], CCOT [9], HCF [19], ADNet ”