Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 Ef๏ฌcient 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. |