Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse
Authors: Kaihao Zhang, Wenhan Luo, Lin Ma, Hongdong Li9203-9210
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on the TU-Berlin dataset show that the proposed model is able to efficiently distill knowledge from natural images and achieves superior performance than the current state of the art. |
| Researcher Affiliation | Academia | Kaihao Zhang,1,3 Wenhan Luo,2 Lin Ma,2 Hongdong Li1,3 1Australian National University 3Australian Centre for Robotic Vision EMAIL quad EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide explicit statements or links for the open-sourcing of the code for their methodology. |
| Open Datasets | Yes | TU-Berlin Dataset. This dataset is proposed in (Eitz, Hays, and Alexa 2012) for sketch recognition, which contains 250 classes of objects. Each class has 80 sketches. TU-Berlin-extend Dataset. The TU-Berlin dataset is extended by adding real images (Zhang et al. 2016). |
| Dataset Splits | No | The paper describes training and testing splits: 'For each protocol, a number of t sketch instances in each category is used for training and the rest sketches in the category are used for testing. Values of t are 16, 24, 32, 40, 48, 56, 64 and 72 for these protocols.' However, it does not explicitly define a separate validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | In the training stage, the input sketch and images are both resized to 256 256. We crop 224 224 3 patches from the resized images and flip them horizontally at random. All weights are initialized as a Gaussian distribution (mean=0 and standard deviation = 0.02). Momentum is set at 0.9. α and β are set to 0.3. The whole training procedure is as follows. Firstly, The CNG-SCN model is trained without warehouse module. Then we fix the above pre-trained model and train the warehouse individually. After that, the CNG-SCN model is combined with warehouse module and fine-turned to obtain a new model. We combine the two kinds of CNGSCN models to make final prediction. |