Automatic Topic Discovery for Multi-Object Tracking
Authors: Wenhan Luo, Björn Stenger, Xiaowei Zhao, Tae-Kyun Kim
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on public data sets we demonstrate the effectiveness of the proposed algorithm. |
| Researcher Affiliation | Collaboration | Wenhan Luo Imperial College London Bj orn Stenger Toshiba Research Europe Xiaowei Zhao Imperial College London Tae-Kyun Kim Imperial College London |
| Pseudocode | No | The paper describes the inference process in text but does not provide a structured pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | For the first problem, we use the public ETHMS and TUD Stadtmitte data sets. For the second problem, we employ public six data sets from (Luo et al. 2014) named Zebra, Crab, Goose, Hockey, Sailing, and Antelope. |
| Dataset Splits | No | The paper mentions using public datasets but does not explicitly provide details about training, validation, or test splits, nor does it refer to standard predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | We set the dictionary dimension to 50 and η to 0.2 in all experiments. In the inference stage, for each epoch we run Gibbs sampling for 500 iterations and report results after the last iteration. |