Learning Precise Temporal Point Event Detection with Misaligned Labels
Authors: Julien Schroeter, Kirill Sidorov, David Marshall9505-9514
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments In order to demonstrate the effectiveness and flexibility of our approach, a broad range of challenging experiments are conducted. |
| Researcher Affiliation | Academia | Julien Schroeter, Kirill Sidorov, David Marshall School of Computer Science & Informatics, Cardiff University, UK {Schroeter J1, Sidorov K, Marshall AD}@cardiff.ac.uk |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available1. 1 https://github.com/Schroeter Julien/AAAI-2021-Learning Precise-Temporal-Point-Event-Detection-with-Misaligned-Labels |
| Open Datasets | Yes | Golf DB: A Video Database for Golf Swing Sequencing (Mc Nally et al. 2019), puff Marker dataset (Saleheen et al. 2015), MAPS database (Emiya, Badeau, and David 2010), IDMT-SMT-Drums dataset (Dittmar and Gärtner 2014) |
| Dataset Splits | Yes | The (4-fold) cross-validated mean accuracy is reported. (Table 1 caption) and The results, produced using ten 6-fold (leave-one-patient-out) cross-validations are summarized in Table 2. |
| Hardware Specification | No | We gratefully acknowledge the support of NVIDIA Corporation with the donation of a GPU used for this research. (This only specifies 'a GPU' from NVIDIA, not a specific model, memory, or other hardware details.) |
| Software Dependencies | No | The paper mentions the 'mir eval library' (Raffel et al. 2014) but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | (SM = 100ms, ατ = max(min( τ 105 , .9), .2).) (Section 6.3) and (learning rate: 10 4, batch size: 32, iterations: 1.5 105, sample length: 1.5s) (Section 6.4) |