|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human.
Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images.
#3 best model for Image Retrieval on CARS196
metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms.
This work considers the problem of domain shift in person re-identification. Being trained on one dataset, a re-identification model usually performs much worse on unseen data.
#3 best model for Person Re-Identification on MSMT17
In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning.
#4 best model for Person Re-Identification on CUHK03
Our algorithm improves one-shot accuracy on ImageNet from 87. 6% to 93. 2% and from 88. 0% to 93. 8% on Omniglot compared to competing approaches.
#3 best model for Few-Shot Image Classification on Mini-ImageNet-CUB 5-way (1-shot)
We propose a new supervized learning framework for oversegmenting 3D point clouds into superpoints.