Sketch Recognition via Part-based Hierarchical Analogical Learning
Authors: Kezhen Chen, Ken Forbus, Balaji Vasan Srinivasan, Niyati Chhaya, Madeline Usher
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the TU Berlin dataset and the Coloring Book Objects dataset show that the system can learn explainable models in a data-efficient manner. |
| Researcher Affiliation | Collaboration | Kezhen Chen1 , Ken Forbus1 , Balaji Vasan Srinivasan2 , Niyati Chhaya2 and Madeline Usher1 1Northwestern University 2Adobe Research kezhenchen@google.com, forbus@northwestern.edu, {balsrini, nchhava}@adobe.com, usher@northwestern.com |
| Pseudocode | Yes | Algorithm 1 Hierarchical Analogical Retrieval |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for the work described, nor does it provide a direct link to a code repository for PHAL. |
| Open Datasets | Yes | We performed experiments on two datasets, the TU Berlin dataset [Eitz et al., 2012] and Coloring Book Objects dataset [Chen et al., 2019]. |
| Dataset Splits | Yes | We used the popular training/testing splits, where each category has 16 testing sketches, and the rest of the sketches are training samples. [...] We use the same cross-validation method described in [Chen et al., 2019] for evaluation. At each round out of ten rounds, a random image in each category is used as the testing samples and the other nine images in each category are used as training samples. |
| Hardware Specification | Yes | Also, our approach only uses up to 10 CPUs to encode sketches and 1 CPU computer to perform hierarchical analogical learning. |
| Software Dependencies | No | The paper mentions software tools like Cog Sketch, Potrace, and Zhang-Suen's thinning algorithm but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | Yes | We performed hyperparameters search, settling on 0.8 as the assimilation thresholds and 0.2 as the cutoff probability for all three encoding levels. On the full dataset, the numbers of categories we keep at each level are 20, 10, and 5. [...] After a hyper-parameter search, we use 0.7 as the assimilation threshold and 0.2 as the cutoff probability for all three levels. During hierarchical analogical retrieval, we keep the top 10, 5, and 3 categories in Levels 1, 2, and 3 respectively. |