Exploring Person Context and Local Scene Context for Object Detection

By Saurabh Gupta, Bharath Hariharan, Jitendra Malik. 2015

25 citations recorded by the source corpus.

In this paper we explore two ways of using context for object detection. The first model focusses on people and the objects they commonly interact with, such as fashion and sports accessories. The second model considers more general object detection and uses the spatial relationships between objects and between objects and scenes. Our models are able to capture precise spatial relationships between the context and the object of interest, and make effective use of the appearance of the contextual region. On the newly released COCO dataset, our models provide relative improvements of up to 5% over CNN-based state-of-the-art detectors, with the gains concentrated on hard cases such as small objects (10% relative improvement).

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Relation Networks for Object Detection (2017, background): “Human centered scenarios Quite a few works focus on human-object relation [51, 22, 20, 21].”
Beyond Skip Connections: Top-Down Modulation for Object Detection (2016, background): “, using other objects [20] and other regions [18] as context.”
Object Detection in 20 Years: A Survey (2019, unknown): “139], MultiPath [140], GBDNet [141, 142], CC-Net [143], MultiRegion-CNN [144], CoupleNet [145], DPM [14, 15], StructDet [146], YOLO [20], RFCN++ [147], ION [148], AttenContext [149], CtxSVM [150], PersonContext [151], SMN [152], RetinaNet [23], SIN [153].”
Contextual Priming and Feedback for Faster R-CNN (2016, background): “, people) as context [68] and using other regions [69].”
Spatial Memory for Context Reasoning in Object Detection (2017, background): “The second type models object-object relationships at instance-level [15, 27, 32, 58, 67, 94].”
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].”
Teaching Compositionality to CNNs (2017, background): “Our environment tends to be highly regular, and making use of regularities in the occurrence of different object and scene classes has been shown to be beneficial for recognizing familiar objects [25, 4], objects in unusual circumstances [3], and recurring spatial configurations [7, 30, 11, 46].”