Modeling mutual context of object and human pose in human-object interaction activities

By Bangpeng Yao, Li Fei-Fei. 2010

645 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, 3D Vision & Pose

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Citations in the corpus

The Open Images Dataset V4 (2018, background): “, 2017b), or focusing on humanobject relations (Gupta et al., 2009; Yao and Fei-Fei, 2010; Prest et al., 2012).”
RGB-D-based action recognition datasets: A survey (2016, background): “Human–object interactivity is another important characteristic of a dataset because some algorithms may benefit from the objects that the actors interact with [60,61,22].”
Learning human activities and object affordances from RGB-D videos (2012, background): “Only recently, some works have shown that modeling mutual context is beneficial (Gupta et al., 2009; Yao and Fei-Fei, 2010).”
Scene Graph Generation by Iterative Message Passing (2017, background): “Much effort has been expended on localizing and recognizing semantic relationships in images [6, 8, 26, 34, 39].”
Unsupervised Semantic Parsing of Video Collections (2015, background): “Also, the previous works on finding action primitives such as [42, 60, 19, 32, 31] are primarily limited to discovering atomic sub-actions, and therefore, fail to identify complex and high-level parts of a long video.”
Visual Relationship Detection with Language Priors (2016, background): “There have been numerous eﬀorts in human-object interaction [20,21,22] and action recognition [23] to learn discriminative models that distinguish be-tween relationships where object 1 is a human ( e.g. “playing violin” [24]).”
Weakly-Supervised Learning of Visual Relations (2017, background): “This task was studied for the special case of human-object interactions [8, 9, 17, 38, 39, 48, 49, 50], where the triplet is in the form ( person, action, object ) .”
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].”
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response (2013, background): “Some works use human pose for action recognition by detecting local pose features [25] and modeling the spatial configuration between human body parts and objects [5], [26], [27], [28], [29].”
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets (2016, methodology): “Some methods have tackled the problem of jointly recognizing actions and poses in videos [20] and still images [37], with the hope to create positive feedback by solving both tasks simultaneously.”
From Lifestyle Vlogs to Everyday Interactions (2017, background): “This area has been extensively studied in vision, typically aiming to infer affordances in still images [12, 14, 32, 47, 46], understand scenes by observing them over time [9, 6], or use them as a building block for scene understanding [20].”
Modeling Visual Context is Key to Augmenting Object Detection Datasets (2018, background): “However, despite these efforts, an improvement due to purely contextual information has always been relatively modest [11, 26].”
DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks (2017, neutral): “object-scene interaction [60], [61], and the presence, location, orientation and size relationship among objects [33], [58], [59], [60], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72].”
Similarity Constrained Latent Support Vector Machine: An Application to Weakly Supervised Action Classification (2012, background): “Yao and Fei-Fei [3] and Yang et al. [13] perform action recognition in images.”
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Exploring Tiny Images: the Roles of Appearance and Contextual Information for Machine and Human Object Recognition (neutral): “Yao et al. [30] exploit contextual interactions between the human pose and objects for activity analysis.”