Contextual Object Detection Using Set-Based Classification

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Object-Proposal Evaluation Protocol is ‘Gameable’ (2015, background): “…become the de-facto pre-processing step in a number of vision pipelines – object detection [13–17], segmentation [18–20], object discovery [21], weakly supervised learning of object-object interactions [22, 23], content aware media re-targeting [24], and action recognition in still images [25].”
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].”
Prime Object Proposals with Randomized Prim's Algorithm (2013, background): “In a similar spirit, recent work has explored applications in object discovery [18], weakly supervised learning of object interaction with humans [19] or with other objects [5], as well as action recognition in still images [22] and content-aware media re-targeting [26].”
Regionlets for Generic Object Detection (2013, methodology): “We do not use any context cues in this paper and leave it as a future work.”
Model recommendation: Generating object detectors from few samples (2015, methodology): “Additional contextual information is thus usually necessary to boost the detection performance [8, 9].”