Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning
Authors: Shanshan Zhao, Xi Li, Omar El Farouk Bourahla
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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). |