Key-segments for video object segmentation

By Yong Jae Lee, Jaechul Kim, Kristen Grauman. 2011

512 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Unsupervised Semantic Parsing of Video Collections (2015, background): “Although any graph clustering approach (e.g. Keysegments [35]) can be applied for this, the joint processing of a large video collection requires handling large visual variability among multiple videos.”
Unsupervised Object Discovery and Tracking in Video Collections (2015, methodology): “Next on our agenda is to address these issues, using for example video stabilization and foreground/background models [19, 20, 25].”
One-Shot Video Object Segmentation (2016, methodology): “We also add the unsupervised techniques: FST [36], SAL [46], KEY [24], MSG [5], TRC [13], CVOS [48], and NLC [10].”
Learning Video Object Segmentation from Static Images (2016, methodology): “In order to overcome these limitations, some methods have proposed to use long-range connections between video frames [15, 25, 48, 55].”
Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation (2018, background): “Prior to the prevalence of deep learning, most approaches to semantic video segmentation are graph based [8, 18, 36, 25].”
Learning Motion Patterns in Videos (2016, methodology): “Measure NLC [9] CVOS [34] TRC [10] MSG [5] KEY [20] SAL [39] FST [27] PCM [3] Ours”
Loosecut: Interactive image segmentation with loosely bounded boxes (2015, background): “One popular approach for unsupervised video segmentation is to detect a set of object proposals, in the form of bounding boxes [12], from each frame and then extract the”
MODNet: Motion and Appearance based Moving Object Detection Network for Autonomous Driving (2018, unknown): “NLC[5] CVOS[27] KEY[14] MSG[3] FST[20] BMM[32] MPNet[29] MPNet[29]+CRF ours ours+CRF mIoU 55.”
Saliency-aware geodesic video object segmentation (2015, methodology): “As the work [15] pointed out, these trajectory-based techniques suffer from the challenges associated with tracking (drift, occlusion and initialization) and clustering (model selection and computational complexity) and lack of prior information for a successful object segmentation.”
Cluster-based Co-saliency Detection (2013, unknown): “Video is treated as a sequence of images, and sometimes the foreground could be defined as the saliency object [53]–[55].”
Instance Embedding Transfer to Unsupervised Video Object Segmentation (2018, methodology): “Classic work in video segmentation produced results using optical flow and shallow appearance models [20, 29, 22, 32, 11] while more recent methods typically pretrain the network Figure 1.”
Predicting Important Objects for Egocentric Video Summarization (2015, methodology): “To model object-like motion , we develop a key-segments video segmentation descriptor [22].”
RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions (2014, methodology): “It has also been used to generate additional features for bounding box detectors [11] and in video segmentation [24].”
Analysing Domain Shift Factors between Videos and Images for Object Detection (2015, unknown): “There are several methods to automatically segment objects from the background in video frames by exploiting spatio-temporal continuity [5], [26], [31], [32].”
Video Segmentation with Superpixels (2012, background): “A large body of literature on VS exists leveraging on appearance [11, 14–16], motion [2, 3, 12], or a combination of cues [10, 13, 17–23].”
MASK-RL: Multiagent Video Object Segmentation Framework Through Reinforcement Learning (2020, background): “…methods typically pose the problem as the identiﬁcation of spatiotemporal “tubes” (representing motion trajectories) [4], [10], [16], [23], [26], [30], [31], [41], [43] by tracking points or patches over consecutive frames in order to localize areas where coherent object motion and…”
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].”
Click Carving: Segmenting Objects in Video with Point Clicks (2016, background): “…from wholly unsupervised bottom-up approaches [1], [2], [3], to propagation methods that exploit user input on the first frame [4], [5], [6], [7], [8], [9], [10], to human-in-the-loop methods where a user closely guides the system towards a good segmentation output [11], [12], [13], [14], [15].”