Summary of Refining Pre-trained Motion Models, by Xinglong Sun et al.
Refining Pre-Trained Motion Models
by Xinglong Sun, Adam W. Harley, Leonidas J. Guibas
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to improving motion estimation models is presented in this paper, which combines self-supervised training with a unique pseudo-labeling strategy. The authors explore the train-test gap in current state-of-the-art methods trained on synthetic data and find that most existing self-supervision techniques actually hinder performance instead of enhancing it. To address this issue, they propose a two-stage approach: first, they estimate motion using a pre-trained model and then select a subset of estimates that can be verified with cycle-consistency, producing a sparse but accurate pseudo-labeling of the video. The model is fine-tuned in the second stage to reproduce these outputs while applying input augmentations. Additionally, simple techniques are used to densify and re-balance the pseudo-labels, ensuring that the training process focuses on diverse and challenging tracks. Experimental results demonstrate reliable gains over fully-supervised methods for both short-term (flow-based) and long-range (multi-frame) pixel tracking in real videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to train machines to understand motion in videos. Right now, the best models are trained using fake data, but they don’t work as well when tested on real videos. Some people have tried training models with real video data, but that hasn’t worked out so well either. The researchers propose a new approach that combines two stages: first, they use a pre-trained model to estimate motion in a video and then select the parts that can be verified by looking at the same scene from different angles. This produces a list of “correct” answers for training the model. They fine-tune the model using these correct answers while adding some noise to the input data. The results show that this approach works better than usual methods when tracking motion in real videos. |
Keywords
» Artificial intelligence » Self supervised » Supervised » Synthetic data » Tracking
