A Graph-based Interactive Reasoning for Human-Object Interaction Detection
Authors: Dongming Yang, Yuexian Zou
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our proposed framework outperforms existing HOI detection methods on both V-COCO and HICO-DET benchmarks and improves the baseline about 9.4% and 15% relatively, validating its efficacy in detecting HOIs. |
| Researcher Affiliation | Academia | Dongming Yang1 and Yuexian Zou1,2 1School of ECE, Peking University, Shenzhen, China 2Peng Cheng Laboratory, Shenzhen, China |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. No links to repositories or explicit statements about code release are found. |
| Open Datasets | Yes | We evaluate our model and compare it with the state-of-the-arts on two large-scale benchmarks, including V-COCO [Yatskar et al., 2016] and HICO-DET [Chao et al., 2018] datasets. |
| Dataset Splits | No | The paper mentions using V-COCO and HICO-DET datasets but does not explicitly provide the training/validation/test split percentages, sample counts, or direct citations to the specific predefined splits they used. |
| Hardware Specification | Yes | All our experiments are conducted by tensorflow on a GPU of Ge Force GTX TITAN X. |
| Software Dependencies | No | The paper mentions 'tensorflow' but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | Yes | We train our model with Stochastic Gradient Descent (SGD), using a learning rate of 1e-4, a weight decay of 1e-4, and a momentum of 0.9. The strategy of interactiveness knowledge training [Li et al., 2019] is adopted in our training and the model is trained for 300K and 1800K iterations on V-COCO and HICO-DET, respectively. |