|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size.
#2 best model for Visual Object Tracking on TrackingNet
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
#3 best model for Visual Object Tracking on YouTube-VOS
During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors.
#9 best model for Visual Object Tracking on VOT2017/18
Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on multiple benchmarks.
#5 best model for Visual Object Tracking on VOT2017/18
We argue that this approach is fundamentally limited since target estimation is a complex task, requiring high-level knowledge about the object.
#3 best model for Visual Object Tracking on TrackingNet
Our algorithm pretrains a CNN using a large set of videos with tracking ground-truths to obtain a generic target representation.
It combines a Convolutional Neural Network (CNN) backbone and a cross-correlation operator, and takes advantage of the features from exemplary images for more accurate object tracking.
#2 best model for Visual Object Tracking on VOT2017
In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.