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Found 7 papers, 4 papers with code
SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving
We show that additional information such as semantics and domain knowledge can help better constrain this problem.
Unsupervised Flow Refinement near Motion Boundaries
Unsupervised optical flow estimators based on deep learning have attracted increasing attention due to the cost and difficulty of annotating for ground truth.
Cross-Attention Transformer for Video Interpolation
We propose TAIN (Transformers and Attention for video INterpolation), a residual neural network for video interpolation, which aims to interpolate an intermediate frame given two consecutive image frames around it.
TDT: Teaching Detectors to Track without Fully Annotated Videos
However, their success depends on the availability of videos that are fully annotated with tracking data, which is expensive and hard to obtain.
Optical Flow Training under Limited Label Budget via Active Learning
Supervised training of optical flow predictors generally yields better accuracy than unsupervised training.
Joint Detection of Motion Boundaries and Occlusions
Since appearance mismatches between frames often signal vicinity to MBs or Occs, we construct a cost block that for each feature in one frame records the lowest discrepancy with matching features in a search range.
Identity Connections in Residual Nets Improve Noise Stability
Residual Neural Networks (ResNets) achieve state-of-the-art performance in many computer vision problems.
