Learning Optical Flow Using Deep Dilated Residual Networks
Bibliographic record
Abstract
Nowadays, convolutional neural networks achieve remarkable performance on optical flow estimation because of its strong non-linear fitting ability. Most of them adopt the U-Net architecture, which contains an encoder part and a decoder part. In the encoder part, the resolution of the feature map is reduced with the deepening of the network layer. In the decoder part, the feature map is enlarged by the deconvolution layer to recover the estimated flow as full resolution. However, the motion details are usually lost with the contracting and expanding operations. Moreover, learning methods, especially supervised networks, always ignore the advantages of many well-proven constraints used in the variational model. In this paper, we introduce a novel architecture named dilated residual networks for learning optical flow, which can avoid the loss of details of the U-Net architecture and can directly learn the residual functions rather than the unreferenced functions to enhance the learning ability of the network. Furthermore, inspired by variational methods, the traditional prior assumptions, such as brightness constancy, gradient constancy, and smoothness assumption, are used in the supervised network as extra auxiliary terms to guide the training of network. Our method is tested on several benchmarks, such as MPI-Sintel, KITTI2012, and KITTI2015. The experimental results show that the dilated residual network is suitable for dense optical flow estimation due to the capability of preserving motion details and can boost the accuracy of optical flow estimation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".