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 [1].
Coarse-to-Fine Lifted MAP Inference in Computer Vision
Authors: Haroun Habeeb, Ankit Anand, Mausam, Parag Singla
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation. |
| Researcher Affiliation | Academia | Haroun Habeeb and Ankit Anand and Mausam and Parag Singla Indian Institute of Technology Delhi EMAIL and EMAIL |
| Pseudocode | Yes | Algorithm 1 Coarse-to-Fine Lifted MAP Algorithm |
| Open Source Code | Yes | We release our implementation for wider use by the community.1https://github.com/dair-iitd/c2fi4cv/ |
| Open Datasets | Yes | We use the benchmark Middlebury Stereo datasets of 2003, 2005 and 2006 [Scharstein and Szeliski, 2003; Hirschmuller and Scharstein, 2007]. For the 2003 dataset, quarter-size images are used and for others, third-size images are used. The label space is of size 85 (85 distinct disparity labels). The dataset used is provided with the implementation. It is a part of the MSRC V2 dataset.6 |
| Dataset Splits | No | The paper mentions using benchmark datasets (Middlebury Stereo and MSRC V2) but does not provide specific details on how these datasets were split into training, validation, and test sets, or if standard splits were used and where they are defined. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions several software components and implementations used (e.g., Open GM2, TSGO implementation, Cooperative Graph Cuts), but it does not specify their version numbers, which are necessary for reproducible software dependency information. |
| Experiment Setup | Yes | For the 2003 dataset, quarter-size images are used and for others, third-size images are used. The label space is of size 85 (85 distinct disparity labels). C2F TSGO uses outputs from the sequence CP(1, 1), CP(2, 1), CP(3, 1) and then refines to the original MRF. Model refinement is triggered whenever energy hasn't decreased in the last four iterations of alpha expansion (this becomes the stopping criteria C in Algorithm 1). |