Hypotheses Tree Building for One-Shot Temporal Sentence Localization
Authors: Daizong Liu, Xiang Fang, Pan Zhou, Xing Di, Weining Lu, Yu Cheng
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two challenging datasets demonstrate that MHST achieves competitive performance compared to existing methods. |
| Researcher Affiliation | Collaboration | 1School of Cyber Science and Engineering, Huazhong University of Science and Technology 2Wangxuan Institute of Computer Technology, Peking University 3Nanyang Technological University 4Protago Labs Inc 5Tsinghua University 6Microsoft Research |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating the availability of its source code. |
| Open Datasets | Yes | Activity Net Captions. This dataset is built from Activity Net v1.3 dataset (Caba Heilbron et al. 2015)... Charades-STA. This dataset is built from the Charades (Sigurdsson et al. 2016) dataset and transformed into temporal sentence localization task by (Gao et al. 2017). |
| Dataset Splits | Yes | We follow the public split of the dataset that contains a training set and two validation sets val 1 and val 2. Following common settings, we use val 1 as our validation set and use val 2 as our testing sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'PyTorch' and specific pre-trained models (C3D, Glove) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For the hyper-parameters, we set the percentage α to 60%, and set the pruning threshold τ as 0.7. The balanced weights λ1, λ2 are set to 1.0,1.0. The step L in L-scan pruning is set to 3. During training, the learning rate is by default 0.00005, and decays by a factor of 10 for every 35 epochs. The batch size is 1 and the maximum training epoch is 100. |