Meta-Auxiliary Learning for Adaptive Human Pose Prediction
Authors: Qiongjie Cui, Huaijiang Sun, Jianfeng Lu, Bin Li, Weiqing Li
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that the proposed approach achieves higher accuracy and more realistic visualization. |
| Researcher Affiliation | Collaboration | Qiongjie Cui1, Huaijiang Sun1*, Jianfeng Lu1, Bin Li2, Weiqing Li1 1Nanjing University of Science and Technology 2Tianjin Ai Forward Science and Technology Co., Ltd., China |
| Pseudocode | Yes | Algorithm 1: Meta-Auxiliary Training |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Dataset-1: H3.6M (Ionescu et al. 2014) involves 15 action categories performed by 7 professional human subjects (S 1, S 5, S 6, S 7, S 8, S 9, S 11). Each pose is represented as a 17-joint skeleton (N = 17), and the sequences are downsampled to achieve 25 fps (Mao et al. 2019; Ma et al. 2022). Dataset-2: We also select 8 action categories from CMU Mo Cap. The pre-processing solution is consistent with the H3.6M dataset. |
| Dataset Splits | Yes | Experimental Setups. We use 3 alternative setups to analyze our model, as stated in Table 1. The prefix S indicates the subject, and C denotes the category. For fairness, we also apply the training/testing division in Table 1, but the hyperparameters remain unchanged, to re-train the baselines. |
| Hardware Specification | No | The paper does not explicitly state specific hardware specifications such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The shared parts consist of 9 residual blocks, created by combining the outputs of SS-RT and TS-RT, and having the channel Cin = Cout = 512. In addition, the task-specific portions of the Pri. and Aux.2 are an additional block to map the feature into the original dimension. By contrast, the Aux.1 is a binary classifier, where its separate parts comprise a flatten layer, and 4 FC layers with channel numbers 256, 128, 64, 1. Aux.1 takes a scrambled-order counterpart of the observation as the input, while for Aux.2, we randomly remove 20% of the joints from observations... we exploit the Adam optimizer to train our network, where the learning rate is initialized to 0.001, with a 0.98 decay every 2 epoch. The mini-batch size is 16. At the test-time adaptation, we fix the learning rate α = β = 2 × 10−5, and 6 gradient descents of Eq.11 are performed. |