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..
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning
Authors: Shanshan Zhao, Xi Li, Omar El Farouk Bourahla
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed approach. |
| Researcher Affiliation | Collaboration | 1 Zhejiang University, Hangzhou, China 2 Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China |
| Pseudocode | Yes | Algorithm 1: Deep Optical Flow Estimation Via MSCSL |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | Flying Chairs [Dosovitskiy et al., 2015] is a synthetic dataset... MPI Sintel [Butler et al., 2012] is created from an animated movie... KITTI 2012 [Geiger et al., 2012] is created from real world scenes... Middlebury [Baker et al., 2011] is a very small dataset... |
| Dataset Splits | Yes | Flying Chairs [...] is split into 22, 232 training and 640 test pairs. [...] we fine-tune the networks on the Clean version and Final version together with 1, 816 for training and 266 for validation. |
| Hardware Specification | Yes | We implement our architecture using Caffe [Jia et al., 2014] and use an NVIDIA TITAN X GPU to train the network. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | We train the networks on Flying Chairs training dataset using Adam optimization with β1 = 0.9 and β2 = 0.999. To tackle the gradients explosion, we adopt the same strategy as proposed in [Dosovitskiy et al., 2015]. Specifically, we firstly use a learning rate of 1e 6 for the first 10k iterations with a batch size of 8 pairs. After that, we increase the learning rate to 1e 4 for the following 300k iterations, and then divide it by 2 every 100k iterations. We terminate the training after 600k iterations (about 116 hours). |