Self-Adaptable Templates for Feature Coding
Authors: Xavier Boix, Gemma Roig, Salomon Diether, Luc V. Gool
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report experiments on two challenging benchmarks for image classification, namely Caltech101 and VOC07. |
| Researcher Affiliation | Academia | 1Computer Vision Laboratory, ETH Zurich, Switzerland 2LCSL, Massachusetts Institute of Technology & Istituto Italiano di Tecnologia, Cambridge, MA |
| Pseudocode | Yes | Algorithm 1: Sparse Quantization in O2P |
| Open Source Code | No | The paper does not provide any link or explicit statement about the availability of its own source code. |
| Open Datasets | Yes | We report results on the Caltech101 [11] and VOC07 [12] datasets |
| Dataset Splits | Yes | We use 3 random splits of 30 images per class for training and the rest for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, cloud instances) are mentioned for running the experiments. |
| Software Dependencies | No | The paper mentions using "LIBLINEAR library for the SVM[20]" but does not provide a specific version number. |
| Experiment Setup | Yes | For Caltech101, the image is re-sized to take a maximum height and width of 300 pixels... We extract SIFT ... at every 8 pixels and at the scales of 16, 32 and 48 pixels diameter... We use a linear one-versus-rest SVM classifier for each class with the parameter C of the SVM set to 1000. |