Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets
Authors: Adarsh Prasad, Stefanie Jegelka, Dhruv Batra
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical contribution is an efficient algorithm for producing a set of image segmentations with significantly higher oracle accuracy than previous works. We apply our greedy maximization algorithms to two image segmentation problems: (1) interactive binary segmentation (object cutout) (Section 4.1); (2) category-level object segmentation on the PASCAL VOC 2012 dataset [11] (Section 4.2). We compare all methods by their respective oracle accuracies, i.e. the accuracy of the most accurate segmentation in the set of M diverse segmentations returned by that method. |
| Researcher Affiliation | Academia | Adarsh Prasad UT Austin adarsh@cs.utexas.edu Stefanie Jegelka UC Berkeley stefje@eecs.berkeley.edu Dhruv Batra Virginia Tech dbatra@vt.edu |
| Pseudocode | No | The paper describes algorithmic steps verbally, but does not include formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not provide an explicit statement or link to its own open-source code for the described methodology. It mentions using 'Cyborg implementation' and referring to other works' implementations for inference, but not its own overall code. |
| Open Datasets | Yes | We apply our greedy maximization algorithms to two image segmentation problems: (1) interactive binary segmentation (object cutout) (Section 4.1); (2) category-level object segmentation on the PASCAL VOC 2012 dataset [11] (Section 4.2). |
| Dataset Splits | Yes | We evaluate all methods on the PASCAL VOC 2012 data [11], consisting of train, val and test partitions with about 1450 images each. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions using specific implementations like the 'Cyborg implementation' [30], 'implementations of Delong et al. [9]', and 'Boykov et al. [3]', but does not provide version numbers for any of these software components. |
| Experiment Setup | Yes | Fifty of the images were used for tuning the diversity parameters λ, γ, and the other 50 for reporting oracle accuracies. The 2-label contrast-sensitive Potts model results in a supermodular relevance function r(y), which can be efficiently maximized via graph cuts [20]. The Hamming ball diversity dlb(y|S) is a collection of cardinality factors, which we optimize with the Cyborg implementation [30]. We construct a multi-label pairwise CRF on superpixels. Our node potentials are outputs of category-specific regressors trained by [6], and our edge potentials are multi-label Potts. Inference in the presence of diversity terms is performed with the implementations of Delong et al. [9] for label costs, Tarlow et al. [30] for Hamming ball diversity, and Boykov et al. [3] for label transitions. Diversity parameters (γ, λ) are chosen by performing cross-val on val. |