Objects in Context

By Andrew Rabinovich, Andrea Vedaldi, Carolina Galleguillos, Eric Wiewiora, Serge J. Belongie. 2007

773 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Attention Augmented Convolutional Networks (2019, background): “els that operate over images. However, the local nature of the convolutional kernel prevents it from capturing global contexts in an image, often necessary for better recognition of objects in images [32]. Self-attention [41], on the other hand, has emerged as a recent advance to capture long range ”
COCO-Stuff: Thing and Stuff Classes in Context (2016, background): “Moreover, several works have shown that stuff is a useful contextual cue to detect things and vice versa [41, 27, 31, 38, 45].”
ParseNet: Looking Wider to See Better (2015, methodology): “In classical computer vision, before the current deep-learning revolution, such context was studied in detail [29, 23].”
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials (2011, methodology): “A common approach is to pose this problem as maximum a posteriori (MAP) inference in a conditional random field (CRF) defined over pixels or image patches [8, 12, 18, 19, 9].”
Not Using the Car to See the Sidewalk — Quantifying and Controlling the Effects of Context in Classification and Segmentation (2018, background): “In computer vision, incorporating context information has been shown to improve performance in various tasks including object recognition [12, 22, 18] and action recognition [9], object detection [3] and segmentation [25].”
Higher Order Conditional Random Fields in Deep Neural Networks (2015, methodology): “…interactions between all pairs of image pixels [24], formulating higher order potentials deﬁned over cliques larger than two nodes [21,27] in order to capture more context, modelling co-occurrence of object classes [14,28,39], and utilising the results of object detectors [29,32,49,51].”
Scene Graph Generation by Iterative Message Passing (2017, background): “Visual scene understanding often harnesses the statistical patterns of object co-occurrence [11, 22, 30, 35] as well as spatial layout [2, 9].”
Visual Relationship Detection with Language Priors (2016, background): “There has been a series of work related to improving object detection by leveraging object co-occurrence statistics [9,10,11,12,13,14].”
Neural Motifs: Scene Graph Parsing with Global Context (2017, methodology): “Our approach is most closely related to work that models object co-occurrence using graphical models to combine many sources of contextual information [30, 11, 23, 10].”
An empirical study of context in object detection (2009, background): “Several recent computer vision approaches have demonstrated that the use of contextual information can lead to substantial improvements in recognition performance [35, 24, 4, 17, 32, 39, 29, 28, 12].”
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].”
Graph R-CNN for Scene Graph Generation (2018, background): “The idea of using context to improve scene understanding has a long history in computer vision [16,27,28,30].”
Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs (2013, background): “Clearly, other image understanding tasks like object detection [10], scene recognition [38], contextual reasoning among objects [29], and pose estimation [39] can aid semantic segmentation.”
Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships (2018, background): “The first type incorporates context around object or scene-level context [3, 43, 37], and the second models object-object relationships at instance-level [18, 4, 30].”
Joint People, Event, and Location Recognition in Personal Photo Collections Using Cross-Domain Context (2010, background): “Rabinovich et al. [13] proposed a CRF model that utilizes object co-occurrence to help object categorization.”
Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora (2010, methodology): “Several methods in computer vision exploit context to recognize objects in scene images (annotation) and to provide a pixelwise localization of these objects (segmentation) [23, 11, 13, 19].”