DASZL: Dynamic Action Signatures for Zero-shot Learning
Authors: Tae Soo Kim, Jonathan Jones, Michael Peven, Zihao Xiao, Jin Bai, Yi Zhang, Weichao Qiu, Alan Yuille, Gregory D. Hager1817-1826
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on the Olympic Sports and UCF101 datasets, where our model establishes a new state of the art under multiple experimental paradigms. |
| Researcher Affiliation | Academia | Tae Soo Kim*, Jonathan Jones* , Michael Peven , Zihao Xiao , Jin Bai , Yi Zhang , Weichao Qiu , Alan Yuille , Gregory D. Hager Johns Hopkins University 3400 N. Charles Street Baltimore, Maryland 21218 {tkim60, jdjones , mpeven, zxiao10, jbai12, yzhang286, wqiu7, ayuille1, hager}@jhu.edu |
| Pseudocode | No | The paper describes algorithms and uses diagrams to illustrate concepts, but it does not include a dedicated pseudocode block or clearly labeled algorithm steps formatted as code. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate our method on the Olympic Sports and UCF101 datasets, where our model establishes a new state of the art under multiple experimental paradigms. We also extend this method to form a unique framework for zero-shot joint segmentation and classification of activities in video and demonstrate the first results in zero-shot decoding of complex action sequences on a widely-used surgical dataset. Lastly, we show that we can use off-the-shelf object detectors to recognize activities in completely de-novo settings with no additional training. ... Olympic Sports (Niebles, Chen, and Fei-Fei 2010) and UCF101 (Soomro et al. 2012) datasets. ... JIGSAWS dataset (Gao et al. 2014) ... DIVA dataset1, which contains fine-grained human-object interactions under a real world video security footage. 1https://actev.nist.gov/ |
| Dataset Splits | No | The paper describes training and test sets and their properties (e.g., 'randomly chosen seen/unseen classes (8/8 for Olympics, 51/50 for UCF101)'), but it does not explicitly provide details about a dedicated validation dataset split, its size, or its specific splitting methodology. |
| Hardware Specification | Yes | We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPUs used for this research. |
| Software Dependencies | No | The paper mentions using certain models or frameworks (e.g., 'pretrained TSM', 'off-the-shelf object detectors (He et al. 2017)') but does not provide specific version numbers for any software dependencies or libraries used in their implementation. |
| Experiment Setup | No | The paper states 'Optimization settings are provided in the supplementary material.', indicating that specific experimental setup details such as hyperparameters are not included in the main text of the paper. |