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].
Face Clustering in Videos with Proportion Prior
Authors: Zhiqiang Tang, Yifan Zhang, Zechao Li, Hanqing Lu
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on a public data set from realworld videos, we observe improvements on clustering performance against state-of-the-art methods. |
| Researcher Affiliation | Academia | Zhiqiang Tang1, Yifan Zhang1 , Zechao Li2, Hanqing Lu1 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2School of Computer Science and Engineering, Nanjing University of Science and Technology EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 EM for Hidden Conditional Random Field |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code for their methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate the performance of our method in the public face data set Big Bang Theory(BBT) given in [Bauml et al., 2013]. |
| Dataset Splits | No | The paper describes sampling faces and computing track labels, but does not explicitly detail a training, validation, and test dataset split for model development or evaluation in the traditional sense, as it's a clustering task. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | We set λ1 = 10 4 in the experiments. λ2 is set to 10 4 in the experiments. ... the k-nearest neighbor graph is used and k = 10. ... The feature of each face is represented by a 240 dimensional Discrete Cosine Transform (DCT) vector. ... Then the Laplacian Eigenmaps reduces the feature dimension from 240 to the cluster number. |