Spatio-Temporal Graph Scattering Transform
Authors: Chao Pan, Siheng Chen, Antonio Ortega
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now evaluate the performance of proposed ST-GST in skeleton-based action recognition task. ... We consider two datasets, MSR Action3D and NTU-RGB+D (cross-subject). ... Tables 1 and 2 compares the classification accuracies on MSR Action3D and NTU-RGB+D, respectively. We see that even without any training, the performance of ST-GST is better than other non-deep-learning and LSTM-based methods, and is comparable with state-of-the-art GCN-based methods in large-scale dataset. |
| Researcher Affiliation | Collaboration | Chao Pan University of Illinois at Urbana-Champaign Champaign, IL, USA chaopan2@illinois.edu; Siheng Chen Shanghai Jiao Tong University Shanghai, China sihengc@sjtu.edu.cn; Antonio Ortega University of Southern California Los Angeles, CA, USA antonio.ortega@ee.usc.edu; This work was mainly done while Chao Pan and Siheng Chen were working at Mitsubishi Electric Research Laboratories (MERL). |
| Pseudocode | No | The paper describes the proposed methods procedurally but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the described methodology or a direct link to a source-code repository. |
| Open Datasets | Yes | MSR Action3D dataset (Li et al., 2010) is a small dataset capturing indoor human actions. ... NTU-RGB+D (Liu et al., 2019) is currently the largest dataset with 3D joints annotations for human action recognition task. |
| Dataset Splits | Yes | Training and testing set is decided by cross-subject split for this dataset, with 288 samples for training and 269 for testing. ... The cross-subject benchmark of NTU-RGB+D includes 40,320 clips for training and 16,560 for testing. |
| Hardware Specification | No | The paper discusses computational efficiency but does not specify any particular hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | The number of layers, L, the number of spatial wavelet scales, Js, and the number of temporal wavelet scales, Jt, are represented by (Js, Jt, L) for separable ST-GST, and (J, L) for joint ST-GST. ... Features output by ST-GST are then utilized by random forest classifier with 300 decision trees for classification. ... Geometric scattering wavelets are used in both domain, and the nonlinear activation σ( ) is absolute value function which has the property of energy-preserving. |