Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics
Authors: Hangjie Yuan, Mang Wang, Dong Ni, Liangpeng Xu3206-3214
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on two popular HOI detection benchmarks demonstrate the significance of incorporating the statistical prior knowledge and produce state-of-the-art performances. |
| Researcher Affiliation | Collaboration | Hangjie Yuan1, Mang Wang3, Dong Ni1,2*, Liangpeng Xu3 1 College of Control Science and Engineering, Zhejiang University, Hangzhou, China 2 State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China 3 DAMO Academy, Alibaba Group, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are available at https://github.com/Jacob Yuan7/OCNHOI-Benchmark. |
| Open Datasets | Yes | In this paper, we use two widely-adopted datasets dubbed HICO-DET (Chao et al. 2015) and V-COCO (Gupta and Malik 2015). |
| Dataset Splits | Yes | HICO-DET contains 37,536 training images and 9,515 testing images... V-COCO contains 2,533 training images, 2,867 validating images and 4,946 testing images... |
| Hardware Specification | Yes | Computational cost analysis on HICO-DET with Tesla V100. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as Python or library versions (e.g., PyTorch 1.9, CUDA 11.1). |
| Experiment Setup | Yes | We adopt Adam W (Loshchilov and Hutter 2018) to optimize OCN for 80 epochs with a weight decay of 10^-4. The learning rate (lr) of the backbone is fixed to 10^-5. The lr of other parts starts from 10^-4 and decays to 10^-5 after the 60th epoch. ... We set number of queries Nq = 100 and the dimension of queries D = 256. The head number H for Inter C and Intra EC is set to 2. The temperature τ of LSKL is set to 0.05. The smoothing parameter β is set to 0.1. ... The hyper-parameters λ1, λ2, λ3, λ4, λ5 in L are set to 1, 2.5, 1, 1, 1 respectively. |