Finding Things: Image Parsing with Regions and Per-Exemplar Detectors

By Joseph Tighe, Svetlana Lazebnik. 2013

227 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

Panoptic Segmentation (2018, methodology): “Later, approaches based on graphical models studied consistent stuff and thing segmentation [50, 41, 42, 40].”
COCO-Stuff: Thing and Stuff Classes in Context (2016, background): “(5) Texture: Stuff classes are typically highly textured [21, 27, 51, 14].”
Deep Interactive Object Selection (2016, methodology): “Many algorithms have been proposed such as superpixel-based [28, 24, 25] and clusteringbased methods [23, 15, 14].”
Scene Parsing through ADE20K Dataset (2017, background): “For example, MRF frameworks are proposed to parse images in different levels [30] or segment rare object classes [33]; detection is combined with segmentation to improve the performance [31]; stuff classes are leveraged to localize objects [13].”
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture (2014, methodology): “Prior work on semantic segmentation includes many different approaches, both using RGB-only data [35, 4, 9] as well as RGB-D [31, 29, 26, 6, 15, 17, 13].”
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation (2015, neutral): “(MRF) [44] 78.”
Learning to Fuse Things and Stuff (2018, background): “This seminal paper inspired a fertile research direction, including contributions on how to explicitly model the relations between things and stuff categories [34, 41, 37, 36, 26].”
SPGNet: Semantic Prediction Guidance for Scene Parsing (2019, background): “Semantic segmentation [21], as a step towards scene understanding [30,53,54,67], is a challenging problem in computer vision.”
DeeperLab: Single-Shot Image Parser (2019, background): “Since then, there have been several works aiming to jointly perform detection and segmentation for whole scene understanding with AND-OR graphs [9, 10], Exemplars [11, 12, 13], or Conditional Random Fields [14, 15, 16, 17, 18, 19].”
MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation (2020, methodology): “This problem has further been studied using graphical models [41], [42], [43].”
Integrating parametric and non-parametric models for scene labeling (2015, unknown): “7%) Tighe[26] 78.”
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation (2016, methodology): “Nonparametric methods have achieved remarkable performance in semantic segmentation [10,11,12,13,14].”
Deep hierarchical parsing for semantic segmentation (2015, background): “These approaches are instances of sophisticated template matching to retrieve images that are visually similar to the query, from a database of labeled images.”
Context Driven Scene Parsing with Attention to Rare Classes (2014, methodology): “Rare classes Per-pixel Per-class Tighe and Lazebnik [21] 48.8 29.9 Our full system 59.4 41.9 Runtime.”
Deep Colorization (2015, background): “As shown in Table I, [25] outperforms other algorithms [34], [35], [36], [37] in terms of pixel accuracy, whether trained by color or grayscale images.”