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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Background Suppression Network for Weakly-Supervised Temporal Action Localization
Authors: Pilhyeon Lee, Youngjung Uh, Hyeran Byun11320-11327
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of Ba S-Net and its superiority over the state-of-the-art methods on the most popular benchmarks THUMOS 14 and Activity Net. |
| Researcher Affiliation | Collaboration | Pilhyeon Lee Yonsei University EMAIL Youngjung Uh Clova AI Research, NAVER Corp. EMAIL Hyeran Byun Yonsei University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and the trained model are available at https://github.com/Pilhyeon/Ba SNet-pytorch. |
| Open Datasets | Yes | We conduct experiments on weakly-supervised temporal action localization task on the most popular benchmarks: THUMOS 14 (Jiang et al. 2014) and Activity Net (Caba Heilbron et al. 2015). |
| Dataset Splits | Yes | We conduct experiments on weakly-supervised temporal action localization task on the most popular benchmarks: THUMOS 14 (Jiang et al. 2014) and Activity Net (Caba Heilbron et al. 2015). We also evaluate our Ba S-Net on Activity Net1.3 in Table 3. Experimental results on Activity Net1.2 are shown in Table 4. |
| Hardware Specification | Yes | Experiments are conducted on a single GTX 1080Ti GPU. |
| Software Dependencies | No | The paper mentions using 'pre-trained feature extractor' and 'TVL1 algorithm' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | All hyperparameters are empirically determined by grid search; r = 8, α = 1, β = 1, γ = 10 4, and θclass = 0.25. For θact, we use a set of thresholds from 0 to 0.5 with the step 0.025 and perform non-maximum suppression (NMS) with threshold 0.7 to remove highly overlapped proposals. |