Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Learning Transferable Subspace for Human Motion Segmentation
Authors: Lichen Wang, Zhengming Ding, Yun Fu
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments based on three human motion datasets illustrate that our approach is able to outperform state-of-the-art temporal data clustering methods. |
| Researcher Affiliation | Academia | Department of Electrical & Computer Engineering, Northeastern University, Boston, USA College of Computer & Information Science, Northeastern University, Boston, USA |
| Pseudocode | Yes | Algorithm 1. Motion Subspace Clustering |
| Open Source Code | No | The paper does not provide any explicit statements about making its own source code publicly available or provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | Multi-modal Action Detection (MAD) Dataset (Huang et al. 2014) ... Keck Gesture Dataset (Jiang, Lin, and Davis 2012) ... Weizmann Dataset (Gorelick et al. 2007) |
| Dataset Splits | No | The paper mentions using '5 randomly selected sequences in source datasets' for evaluation and concatenating data for certain datasets, but it does not explicitly specify training, validation, and test dataset splits with percentages, sample counts, or a detailed splitting methodology. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'HoG features (Dalal and Triggs 2005)' but does not list any specific software or library dependencies with version numbers used for its implementation. |
| Experiment Setup | Yes | The parameter values λ1 and λ2 are set to be 0.015 and 12 as the default, the correlated frame distance s = 9 and the projection size r = 80. Parameter sensitivity is analyzed later in this section. |