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..
Volumetric Correspondence Networks for Optical Flow
Authors: Gengshan Yang, Deva Ramanan
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our innovations dramatically improve accuracy over SOTA on standard benchmarks while being significantly easier to work with training converges in 7X fewer iterations |
| Researcher Affiliation | Collaboration | Gengshan Yang1 , Deva Ramanan1,2 1Carnegie Mellon University, 2Argo AI EMAIL |
| Pseudocode | No | Not found. The paper describes methods and components but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code will be available at github.com/gengshay-y/VCN. |
| Open Datasets | Yes | Number of training iterations is recorded for the pre-training stage on Flying Chairs and Flying Things, and (S) indicates sequential training on separate modules. |
| Dataset Splits | No | Number of training iterations is recorded for the pre-training stage on Flying Chairs and Flying Things, and (S) indicates sequential training on separate modules. As shown in Tab. 2, after the pretraining stage, ours-small achieves smaller end-point-error (EPE) than all methods on KITTI [9, 30] |
| Hardware Specification | Yes | The model is trained on a machine with 4 Titan X Pascal GPUs. |
| Software Dependencies | No | We build the model and re-implement the training pipeline of PWC-Net+ [39] using Pytorch. |
| Experiment Setup | Yes | We find correspondences with 9 9 search windows on a feature pyramid with stride {64, 32, 16, 8, 4}. We keep K = {16, 16, 16, 16, 12} hypotheses at each scale. To be noted, we are able to stably train the network with a larger learning rate (10 3 vs 10 4) and fewer iterations (140K vs 1200K on Flying Chairs and 80K vs 500K on Flying Things) compared to prior optical flow networks. |