Sketch Recognition with Natural Correction and Editing
Authors: Jie Wu, Changhu Wang, Liqing Zhang, Yong Rui
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the effectiveness of the proposed algorithms. |
| Researcher Affiliation | Collaboration | 1Brain-Like Computing Lab, Shanghai Jiao Tong University, P. R. China 2Microsoft Research, Beijing, P. R. China |
| Pseudocode | No | The paper describes algorithmic steps in narrative text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We also tested the proposed method in a public benchmark dataset HHReco (Hse and Newton 2004).; In this task, we added correction/editing into hand-drawn full diagrams in the public benchmark dataset (Lemaitre et al. 2013) |
| Dataset Splits | Yes | The leave-one-out cross validation was performed. |
| Hardware Specification | Yes | on a PC with an Intel Core i7-2600 CPU. |
| Software Dependencies | No | The paper mentions software components like 'IDM recognizer' but does not specify any version numbers for these or other ancillary software components (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | No | The paper describes the general experimental setup (e.g., tasks, datasets used, cross-validation), but it does not provide specific hyperparameters (like learning rates, batch sizes, number of epochs) or detailed system-level training configurations. |