Bilinear heterogeneous information machine for RGB-D action recognition

By Yu Kong, Yun Raymond Fu. 2015

82 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Wanqing Li
2. Pichao Wang
3. Jun Liu
4. G. Wang
5. Hossein Rahmani
6. Philip O. Ogunbona
7. Zhimin Gao
8. Amir Shahroudy

In Computer Vision and Pattern Recognition

1. Jun Liu
2. G. Wang
3. Wanqing Li
4. Pichao Wang
5. Hossein Rahmani
6. Amir Shahroudy
7. Qiuhong Ke
8. Philip O. Ogunbona

Citations in the corpus

• NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis (2016, background): “Each pair contains very closely re-lated actions with differences along temporal axis e.g . pick up/put down a box, push/pull a chair, wear/take off a hat, etc. State-of-the-art methods [17, 32, 31] achieved perfect accuracy on this benchmark.”
• NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, neutral): “State-of-the-art methods [38], [43], [44] achieved perfect accuracy on this benchmark.”
• Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016, methodology): “Our method ties with two recent works (MMMP [54], and BHIM [22]) in saturating the benchmark by achieving the flawless 100% accuracy on this dataset.”
• Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, methodology): “These methods either extracted features from one modality: depth [49, 60, 29, 59, 24, 51, 52] or RGB [27, 33] or skeleton [43, 55, 56, 4, 34, 19], or fuse the features extracted separately from them at a later stage [28, 16, 10, 58].”
• A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets (2016, background): “In the case of RGBD videos, researchers have proposed the fusion of depth and color features [9, 14].”
• Multi-Stream Deep Neural Networks for RGB-D Egocentric Action Recognition (2019, background): “Similarly, recent advances on RGB-D action recognition [15], [16], [19], [67], [68] have also focused on mining the complementary information between the RGB”
• Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “Early methods [346], [347] focused on extracting”
• Jointly Learning Heterogeneous Features for RGB-D Activity Recognition (2015, background): “Alternatively, some methods assume that different heterogeneous features share in the primitive feature space, a common subspace or even a common subset of input primitive features (without explicitly considering specific structures of each feature type) [9], [14],”