Recognition using visual phrases

By Mohammad Amin Sadeghi, Ali Farhadi. 2011

467 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Learning Non-maximum Suppression (2017, background): “One line of work proposes to detect pairs of objects instead of each individual objects in order to handle strong occlusion [24, 29, 19].”
End-to-End People Detection in Crowded Scenes (2015, background): “To overcome the difficulties of predicting multiple objects in close proximity, several attempts have been made to jointly predict constellations of objects [5, 23, 15].”
CIDEr: Consensus-based image description evaluation (2014, neutral): “Finally, we compare performance of humans and CIDEr at predicting consensus.”
Generation and Comprehension of Unambiguous Object Descriptions (2015, background): “[46]), our deep learningbased models are able to learn to directly generate surface expressions from raw images without having to first convert to a formal object/attribute representation.”
Unsupervised Discovery of Mid-Level Discriminative Patches (2012, methodology): “Others have employed discriminative part detectors such as poselets [17], at-Unsupervised tribute detectors [18], “visual phrases” [19], or “stuﬀ” detectors [20] as features.”
SUN Database: Exploring a Large Collection of Scene Categories (2014, background): “Also, while our approach takes scene representations and makes them more local, complementary work from [37] comes from the other direction and detects object arrangements called “visual phrases”.”
Multimodal Convolutional Neural Networks for Matching Image and Sentence (2015, background): “Early work usually focuses on modeling the relation between the image and annotating words [6, 9, 31, 32, 35] or phrases [27, 37].”
Scene Graph Generation by Iterative Message Passing (2017, methodology): “One of the most popular ways of representing a visual scene is through text descriptions [14, 34, 44].”
Exploring Visual Relationship for Image Captioning (2018, background): “, actions or interactions) between objects are explored in [6,32] where each possible combination of semantic relation is taken as a visual phrase class and the visual relationship detection is formulated as a classification task.”
Choosing Linguistics over Vision to Describe Images (2012, neutral): “Conceptually, our work closely relates to Sadeghi and Farhadi (2011).”
Visual Relationship Detection with Language Priors (2016, methodology): “icitly collected relationships in images [25,26,27,28,29] and videos [27,30,31] and helped models map these relationships from images to language. Relationships have also improved object localization [32,33,6,34]. A meaning space of relationships have aided the cognitive task of mapping images to ca”
Weakly-Supervised Learning of Visual Relations (2017, methodology): “We compare with the Visual Phrases model [43] and several variants of [31] 1 : Visual model alone (b.), Language (likelihood of a relationship) (c.), combined Visual+Language model (d.).”
From generic to specific deep representations for visual recognition (2014, background): “non-ConvNet [35] [22] [40] [26] [38] [45] [25] [12] [18] [24] [42] [30] [37] [46] [37] [37] [4] 71.”
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection (2014, background): “The context information inves tigated in previous works includes regions surrounding objects [9, 12, 22], object-scene interaction [ 13], and the presence, location, orientation and size relationship among ob jects [3, 57, 58, 11, 41, 22, 49, 13, 61, 12, 59, 40, 10, 45, 51].”
Detecting and Recognizing Human-Object Interactions (2017, background): “Research on visual relationship modeling [29, 14, 23, 34] has attracted increasing attention.”
Visual Storytelling (2016, background): “, 2015; Malinowski and Fritz, 2014), visual phrases (Sadeghi and Farhadi, 2011), video understanding (Ramanathan et al.”
Learning the Visual Interpretation of Sentences (2013, background): “Several papers have also explored the use of various forms of tuples for use in semantic scene understanding [8, 31, 19].”