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..
RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection
Authors: Dongming Yang, Yuexian Zou, Can Zhang, Meng Cao, Jie Chen
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our proposed RR-Net sets a new state-of-the-art on both V-COCO and HICO-DET benchmarks and improves the baseline about 5.5% and 9.8% relatively, validating that this ο¬rst effort in exploring relation reasoning and integrating interactive semantics has brought obvious improvement for end-to-end HOI detection. |
| Researcher Affiliation | Academia | Dongming Yang1 , Yuexian Zou1,2 , Can Zhang1 , Meng Cao1 and Jie Chen1,2 1School of ECE, Peking University, Shenzhen, China, 518055 2Peng Cheng Laboratory, Shenzhen, China, 518055 |
| Pseudocode | No | The paper describes the proposed method in detail using prose and mathematical formulations, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or include a link to a code repository. |
| Open Datasets | Yes | We evaluate our method 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 and training details like batch size and epochs, but it does not specify the exact training/validation/test splits (e.g., percentages or sample counts) used for reproduction. |
| Hardware Specification | Yes | Our experiments are conducted by Pytorch on a single GPU of NVIDIA Tesla P100. |
| Software Dependencies | No | The paper states 'Our experiments are conducted by Pytorch'. However, it does not provide specific version numbers for Pytorch or any other software dependencies. |
| Experiment Setup | Yes | During training, input images have the resolution of 512 512, yielding a resolution of 128 128 for all output head features. We employ standard data augmentation following [Zhou et al., 2019]. Our model is optimized with Adam. The batch-size is set as 15 for VCOCO and 20 for HICO-DET. We train the model for 140 epochs, with the initial learning rate of 5e-4 which drops 10x at 90 and 120 epochs respectively. The top predictions T is set as 100. |