DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection
Authors: Hao-Shu Fang, Yichen Xie, Dian Shao, Cewu Lu1291-1299
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two popular benchmarks: V-COCO and HICO-DET show that our approach outperforms existing state-of-the-arts by a large margin with the highest inference speed and lightest network architecture. |
| Researcher Affiliation | Academia | 1 Shanghai Jiao Tong University 2 The Chinese University of Hong Kong |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at www.github.com/MVIG-SJTU/DIRV. |
| Open Datasets | Yes | We evaluate our method on two popular datasets: V-COCO (Gupta and Malik 2015) and HICO-DET (Chao et al. 2015). |
| Dataset Splits | Yes | V-COCO dataset is a subset of COCO (Lin et al. 2014) with extra interaction labels. It contains 10,346 images (2,533 for training, 2867 for validation and 4,946 for testing). |
| Hardware Specification | Yes | All experiments are carried out on NVIDIA RTX2080Ti GPUs. |
| Software Dependencies | No | The paper mentions software like Efficient Det-d3 and Adam optimizer but does not provide specific version numbers for key libraries or frameworks (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | We set the learning rate as 1e-4 with a batch size of 32. |