WebOfficial PyTorch implementation of "Robust Online Tracking with Meta-Updater". (IEEE TPAMI) - Meta-Updater/README.md at master · zj5559/Meta-Updater WebThis paper improves state-of-the-art on-line trackers that use deep learning. Such trackers train a deep network to pick a specified object out from the background in an initial frame (initialization) and then keep training the model as tracking proceeds (updates). Our core contribution is a meta-learning-based method to adjust deep networks for tracking using …
arXiv:1909.02959v2 [cs.CV] 21 Nov 2024
WebThis paper improves state-of-the-art visual object trackers that use online adaptation. Our core contribution is an offline meta-learning-based method to adjust the initial deep networks used in online adaptation-based… WebFigure 1. Overview of meta-training for visual object tracking: A computational graph in meta-training object trackers at the initial frame. It gets the gradients of the loss on the first frame, and a meta-updater updates parameters of … first agro-industrial rural bank inc
Title: Meta-Tracker: Fast and Robust Online Adaptation for Visual ...
WebJan 9, 2024 · Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers. Eunbyung Park, Alexander C. Berg. This paper improves state-of-the-art visual object trackers that use online adaptation. Our core contribution is an offline meta-learning-based method to adjust the initial deep networks used in online adaptation-based tracking. WebJan 9, 2024 · Most online trackers, including the two trackers we meta-train (Section 4), update the target model regularly to adjust to new examples collected by itself during tracking. We could simply use meta-trained α to update the model, θ j = θ j − 1 − α ⊙ ∇ θ j − 1 L (only one iteration presented for brevity). However, it often diverges ... WebWe integrate our meta-updater into eight different types of online update trackers. Extensive experiments on four long-term and two short-term tracking benchmarks demonstrate that … firstaid 1.9.8