Balancing Appearance and Context in Sketch Interpretation
Authors: Yale Song, Randall Davis, Kaichen Ma, Dana L. Penney
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We calibrate the utility of different forms of context, describing experiments with Conditional Random Fields trained and tested using a variety of features. |
| Researcher Affiliation | Collaboration | Yale Song1, Randall Davis2, Kaichen Ma3, Dana L. Penney4 1Yahoo Research, 2Massachusetts Institute of Technology, 3Google Inc., 4Lahey Hospital and Medical Center |
| Pseudocode | Yes | Algorithm 1 Detecting Overwriting and Augmentation; Algorithm 2 Repair Strategy for Under-segmentation; Algorithm 3 Repair Strategy for Over-segmentation |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository. |
| Open Datasets | No | We used a set of 2,024 clock drawings collected from clinical facilities: 1,654 clocks drawn by healthy participants and 370 clocks randomly selected from mildly cognitively impaired participants. We have accumulated a database of several thousand tests from both healthy and cognitively impaired subjects, a unique dataset of real-world drawing behavior. |
| Dataset Splits | Yes | To avoid over-fitting, we used an l2 regularization with its scale term set at 0.01, chosen based on 10-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'boosted logistic regression classifier' and 'sketched symbol recognizer of [Ouyang and Davis, 2009b]' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | To avoid over-fitting, we used an l2 regularization with its scale term set at 0.01, chosen based on 10-fold cross validation. |