Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals

By Fanyi Xiao, Yong Jae Lee. 2016

110 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Learning Unsupervised Video Object Segmentation Through Visual Attention (2019, background): “Early UVOS methods are typically based on handcrafted features and heuristics such as long-term point trajectory [54, 5, 17, 53, 9], motion boundary [56], objectness [43, 51, 89, 18, 59, 83, 40, 45], and saliency [13, 77, 76, 34, 27].”
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection (2016, methodology): “For each video in SV , we take the 9 highest-ranked tracks generated by [40].”
VideoMatch: Matching based Video Object Segmentation (2018, background): “Video object segmentation has been extensively studied in the past [49, 31, 36, 44, 30, 29, 39, 55, 15, 50, 22, 5, 25].”
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos (2020, methodology): “Video Segmentation: Temporally consistent object segmentation in videos can benet several other tasks such as action/activity recognition [24,30], video object detection [37,21] and object discovery [91,61,82,40]. Several bottom-up methods segment moving regions by grouping based on optical ow [1,92”
A Survey on Deep Learning Technique for Video Segmentation (2021, methodology): “Several methods [5], [38], [39], [40] explored object hypotheses or proposals [41] as middle-level object representations.”
Lucid Data Dreaming for Object Tracking (2017, methodology): “Box-to-segment: First a box-level track is built, and a space-time grabcut-like approach is used to generate per frame segments [63].”
Saliency-Aware Video Object Segmentation (2018, methodology): “Table 2 presents the IoU scores of our method and [7], [9], [22], [25], [27], [29], [63], [64], [65], [66] on the SegTrack and the extended SegTrack.”
Learning About Objects by Learning to Interact with Them (2020, background): “[55, 64, 60] require a video at test time to find the moving objects.”
Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation (2018, background): “ing studies [31, 34, 15, 26, 33, 16] exploit temporal information to maintain continuity of individual segmented regions across frames.”