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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Self-Reinforced Cascaded Regression for Face Alignment
Authors: Xin Fan, Risheng Liu, Kang Huyan, Yuyao Feng, Zhongxuan Luo
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments were performed on six widely used datasets include FRGC v2.0, LFPW, HELEN, AFW, i BUG and 300W. All faces are labeled 68 landmarks. We compute the alignment error for testing images using the standard mean error normalized by the inter-pupil distance (NME). |
| Researcher Affiliation | Academia | 1DUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian, China 2Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China 3School of Mathematical Science, Dalian University of Technology, Dalian, China EMAIL, EMAIL EMAIL |
| Pseudocode | No | The paper includes mathematical formulations and figures but no explicitly labeled "Pseudocode" or "Algorithm" blocks with structured steps. |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code or provide any links to a code repository. |
| Open Datasets | Yes | The experiments were performed on six widely used datasets include FRGC v2.0, LFPW, HELEN, AFW, i BUG and 300W. and The 300W set consisting of the test sets of LFPW and Helen (Le et al. 2012). |
| Dataset Splits | Yes | We started from 100 labeled examples, and implemented the self-reinforced version of LBF (SR-LBF) to automatically include 711 extra samples (regarded as unlabeled). and In contrast, our self-reinforced LBF (SR-LBF) starts from only a half of LFPW, i.e. 400 training labels, and the other half are included by our self-reinforced strategy. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various algorithms and feature descriptors but does not list any specific software dependencies (e.g., libraries, frameworks) along with their version numbers. |
| Experiment Setup | No | The paper mentions some parameters like μ and λ but does not provide specific hyperparameter values (e.g., learning rate, batch size, epochs, optimizer settings) or detailed training configurations for the experimental setup. |