Combining multiple sources of knowledge in deep CNNs for action recognition

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Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection (2017, background): “…- LTC [38] 91.7% 64.8% - Two-stream [33] 88.0% 59.4% - TSN [43] 94.2 % 69.4% - CPD [26] 92.3% 66.2% - Multi-Granular [18] 90.8% 63.6% - M-fusion [28] 89.1% 54.9% - KVMF [49] 93.1% 63.3% - P-CNN [4] - - 61.1% Action tubes [9] - - 62.5% TS R-CNN [29] - - 70.5% MR-TS R-CNN [29] - - 71.1% Ours…”
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter (2017, methodology): “For this we rely on the “late fusion” approach that has been shown to be effective in vision tasks (Karpathy et al., 2014; Park et al., 2016).”
Expert knowledge-infused deep learning for automatic lung nodule detection (2018, unknown): “…of computer vision, to overcome the challenges of the limited existing datasets and the difficulty for network to learn various types of robust features simultaneously, some researchers have proposed methods to incorporate the hand-crafted features in combination with CNN based models ([22–27]).”
Semantic-Aware Scene Recognition (2019, background): “, depth, optical flow or heat maps) as additional sources of information to multi-modal classification models has been recently used to boost different computer vision tasks: pedestrian detection [35–38], action recognition [39, 40] and hand-gesture recognition [41].”