RepNet - Counting Out Time - Class Agnostic Video Repetition Counting in the Wild

Explore a comprehensive video explanation of the RepNet paper, which introduces a groundbreaking approach to counting repeated actions in videos. Dive into the innovative use of temporal self-similarity matrices as an information bottleneck, forcing the model to extract relevant information for counting. Learn about the synthetic dataset creation process and the new Countix dataset for evaluating counting models. Discover the state-of-the-art performance achieved by RepNet on existing periodicity and repetition counting benchmarks. Follow along as the video breaks down the problem statement, output and loss functions, per-frame embeddings, periodicity predictor, and overall architecture. Gain insights into the experiments conducted, potential applications, and concluding thoughts on this class-agnostic video repetition counting method.

- Intro & Overview
- Problem Statement
- Output & Loss
- Per-Frame Embeddings
- Temporal Self-Similarity Matrix
- Periodicity Predictor
- Architecture Recap
- Synthetic Dataset
- Countix Dataset
- Experiments
- Applications
- Conclusion & Comments