Summary of Is Multiple Object Tracking a Matter Of Specialization?, by Gianluca Mancusi et al.
Is Multiple Object Tracking a Matter of Specialization?
by Gianluca Mancusi, Mattia Bernardi, Aniello Panariello, Angelo Porrello, Rita Cucchiara, Simone Calderara
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The paper introduces PASTA (Parameter-efficient Scenario-specific Tracking Architecture), a novel framework for training end-to-end transformer-based trackers in heterogeneous scenarios. The challenges of negative interference and limited domain generalization are addressed by combining Parameter-Efficient Fine-Tuning (PEFT) with Modular Deep Learning (MDL). This allows for systematic generalization to new domains without increasing inference time. Experiments on MOTSynth, MOT17, and PersonPath22 demonstrate that a tracker built from carefully selected modules outperforms its monolithic counterpart. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PASTA is a new way to train trackers that can work well in different scenarios. Right now, trackers are great at tracking people in certain situations, but they struggle when the situation changes. The problem is that the model learns things that don’t help it track in other situations. PASTA solves this by breaking down the task into smaller parts and training each part to be good at a specific type of scenario. This lets the tracker work well in new situations without needing more data or training. |
Keywords
» Artificial intelligence » Deep learning » Domain generalization » Fine tuning » Generalization » Inference » Parameter efficient » Tracking » Transformer
