Incorporating Scene Context and Object Layout into Appearance Modeling

By Hamid Izadinia, Fereshteh Sadeghi, Ali Farhadi. 2014

47 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

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations (2016, background): “Relative layouts between objects have improved scene categorization (Izadinia et al. 2014), and 3D spatial geometry between objects has helped object detection (Choi et al.”
Visual Relationship Detection with Language Priors (2016, background): “Structured learning approaches have improved scene classiﬁcation along with object detection using hierarchial contextual data from co-occurring objects [15,16,17,18].”
Representation Learning for Visual-Relational Knowledge Graphs (2017, background): “Recent years have witnessed a surge in reasoning about human-object, object-object, and object-attribute relationships [30,31,32,33,20,34,35,36].”
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation (2018, background): “…phrases detection and detection works and these works were mostly supposed to leveraging other tasks, such as object recognition [4,12,13,31,32,44,51,53,55], image classiﬁcation and retrieval [17,39], scene understanding and generation [2,3,18,23,24,61,65], as well as text grounding [27, 43, 49].”
VisKE: Visual knowledge extraction and question answering by visual verification of relation phrases (2015, background): “Recent years have also witnessed a surge in reasoning about human-object relationships [19, 39] as well as more general object-object relationships [16, 23, 24, 25] and object-attribute relationships [6, 15, 40].”
Scene Graph Generation With External Knowledge and Image Reconstruction (2019, background): “With recent breakthroughs in deep learning and image recognition, higher-level visual understanding tasks, such as visual relationship detection, has been a popular research topic [9, 19, 15, 40, 44].”
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs (2017, background): “[8, 26, 29, 33] which are required for more complex visual-relational reasoning. Recent years have witnessed a surge in reasoning about human-object, object-object, and object-attribute relationships [14, 9, 22, 35, 10, 6, 16, 37]. The VisualGenome project [19] is a knowledge base that integrates la”