Detecting Human-Object Interactions via Functional Generalization
Authors: Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa10460-10469
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive experimental validation for our approach and demonstrate state-of-the-art results for HOI detection. On the HICO-Det dataset our method achieves a gain of over 2.5% absolute points in mean average precision (m AP) over stateof-the-art. |
| Researcher Affiliation | Academia | Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa University of Maryland, College Park {ankan, rssaketh, abhinav, rama}@umiacs.umd.edu |
| Pseudocode | No | The paper describes methods textually and refers to figures, but it does not include a dedicated pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | Yes | We evaluate our approach on the HICO-Det dataset (Chao et al. 2015). |
| Dataset Splits | Yes | The training set contains over 38,000 images and about 120,000 HOI annotations for the 600 HOI classes. The test set has 33,400 HOI instances. [...] Performance is usually reported for three different HOI category sets: (a) all 600 classes (Full), (b) 138 classes with less than 10 training samples (Rare), and (c) the remaining 462 classes with more than 10 training samples (Non-Rare). |
| Hardware Specification | No | The paper mentions training models and using a Res Net-101 backbone Faster-RCNN, but it does not specify any hardware details such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions using word2vec vectors and a Faster-RCNN, but it does not provide specific version numbers for any software components or libraries used in the experiments. |
| Experiment Setup | Yes | For all the experiments, we train the model for 25 epochs with 0.1 initial learning rate which is dropped by a tenth every 10 epochs. |