Neighbourhood Consensus Networks
Authors: Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, Josef Sivic
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approach was evaluated on both instance- and category-level matching problems. The same approach is used to obtain reliable matches for both problems, which are then used to solve two completely different tasks: (i) camera pose estimation in the challenging scenario of indoor localization, in the instance-level matching case, and (ii) semantic object alignment in the category-level matching case. |
| Researcher Affiliation | Collaboration | Ignacio Rocco Mircea Cimpoi Relja Arandjelovi c Akihiko Torii Tomas Pajdla Josef Sivic , Inria CIIRC, CTU in Prague Deep Mind Tokyo Institute of Technology |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Both training code and models are available at [1]. [1] Project webpage (code/networks). http://www.di.ens.fr/willow/research/ncnet/. |
| Open Datasets | Yes | We report results on the PF-Pascal [11] dataset, which contains 1,351 semantically related image pairs from the 20 object categories of the PASCAL VOC [9] dataset. ... We evaluate on the In Loc dataset [42]... As the In Loc dataset was designed for evaluation and does not contain a training set, we collected an Indoor Venues Dataset, consisting of user-uploaded photos... The design and collection procedures are described in the supplementary material [30] and the dataset is available at [1]. |
| Dataset Splits | Yes | We follow the same evaluation protocol as [12, 29], and use the split from [12] which divides the dataset into approximately 700 pairs for training, 300 for validation and 300 for testing. ... This dataset contains 3861 positive image pairs from 89 different venues in 6 different cities, split into train: 3481 pairs (80 places) and validation: 380 pairs (from the remaining 9 places). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'implemented in Py Torch' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The model is initially trained for 5 epochs using Adam optimizer [20], with a learning rate of 5 10 4 and keeping the feature extraction layer weights fixed. For category level matching, the model is then subsequently finetuned for 5 more epochs, training both the feature extraction and the neighbourhood consensus network, with a learning rate of 1 10 5. |