Temporally Grounding Language Queries in Videos by Contextual Boundary-Aware Prediction
Authors: Jingwen Wang, Lin Ma, Wenhao Jiang12168-12175
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on three public datasets: TACo S (Regneri et al. 2013), Charades-STA (Gao et al. 2017), and Activity Net Captions (Krishna et al. 2017).Table 1: Performance comparison on TACo S (Regneri et al. 2013) dataset. All results are reported in percentage (%).Table 2: Ablation study on TACo S (Regneri et al. 2013) dataset. All results are reported in percentage (%). |
| Researcher Affiliation | Industry | Jingwen Wang, Lin Ma, Wenhao Jiang Tencent AI Lab {jaywongjaywong, forest.linma, cswhjiang}@gmail.com |
| Pseudocode | No | The paper describes its proposed method and training process in text and figures but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is released or available. |
| Open Datasets | Yes | We conduct extensive experiments on three public datasets: TACo S (Regneri et al. 2013), Charades-STA (Gao et al. 2017), and Activity Net Captions (Krishna et al. 2017). |
| Dataset Splits | Yes | The same split as (Gao et al. 2017) is used, which includes 10146, 4589, 4083 query-segment pairs for training, validation and testing.The train/test split is 12408/3720.we merge the two validation subsets val1, val2 as our test split, as (Chen et al. 2018). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions several software components like C3D features, GloVe word embeddings, Match-LSTM, and LSTM, but does not provide specific version numbers for any of these or the underlying frameworks used for implementation. |
| Experiment Setup | Yes | We set hidden neuron size of LSTM to 512. We generally design the K anchors to cover at least 95% of training segments. Therefore, we empirically set K to 32, 20 and 100 for TACo S, Charades-STA and Activity Net Captions, respectively. The NMS thresholds are 0.3, 0.55 and 0.55, respectively. |