Background Suppression Network for Weakly-Supervised Temporal Action Localization
Authors: Pilhyeon Lee, Youngjung Uh, Hyeran Byun11320-11327
AAAI 2020 | Venue PDF | Archive PDF | Plain Text | 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. |