Action Sequence Augmentation for Action Anticipation
Authors: Yihui Qiu, Deepu Rajan
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on the 50Salads, EGTEA Gaze+, and Epic-Kitchens-100 datasets demonstrate significant performance improvements over existing state-of-the-art methods. |
| Researcher Affiliation | Academia | Yihui Qiu & Deepu Rajan College of Computing and Data Science Nanyang Technological University, Singapore EMAIL |
| Pseudocode | No | The paper describes the Action Grammar Induction (AGI) algorithm and the Cross-Tree Earley Parser (CTEP) operations in text and bullet points, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing code or links to source code repositories for the methodology described. |
| Open Datasets | Yes | We perform experiments on three action anticipation benchmarks. 50Salads Stein & Mc Kenna (2013) consists of 901 action annotations, and 17 action classes. EGTEA Gaze+ Li et al. (2021) contains 10,325 action annotations, 19 verbs, 51 nouns and 106 action classes. EPIC-Kitchens-100 Damen et al. (2022) contains 3806 actions, with 97 verbs, and 300 nouns. |
| Dataset Splits | Yes | We report the average performance across the standard five splits. Methods are evaluated on EGTEA Gaze+ reporting the average performance across the three splits provided by the authors of the dataset. We evaluate our method on the validation dataset following previous work Guo et al. (2024). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper discusses augmentation strategies and their impact on performance, but does not explicitly provide concrete hyperparameter values or detailed training configurations (e.g., learning rate, batch size, number of epochs, optimizer settings) for the models used in the experiments. |