Few-Shot Video Object Detection
arXiv:2104.14805
Abstract
We introduce Few-Shot Video Object Detection (FSVOD) with three contributions to real-world visual learning challenge in our highly diverse and dynamic world: 1) a large-scale video dataset FSVOD-500 comprising of 500 classes with class-balanced videos in each category for few-shot learning; 2) a novel Tube Proposal Network (TPN) to generate high-quality video tube proposals for aggregating feature representation for the target video object which can be highly dynamic; 3) a strategically improved Temporal Matching Network (TMN+) for matching representative query tube features with better discriminative ability thus achieving higher diversity. Our TPN and TMN+ are jointly and end-to-end trained. Extensive experiments demonstrate that our method produces significantly better detection results on two few-shot video object detection datasets compared to image-based methods and other naive video-based extensions. Codes and datasets are released at \url{https://github.com/fanq15/FewX}.
ECCV 2022
References in corpus (6)
- High-Speed Tracking with Kernelized Correlation Filters
- Prototypical Networks for Few-shot Learning
- GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
- Frustratingly Simple Few-Shot Object Detection
- LSTD: A Low-Shot Transfer Detector for Object Detection
- Cooperating RPN's Improve Few-Shot Object Detection