|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
We turn it into a realistic few-shot classification benchmark by splitting the object categories into head and tail based on their distribution in the world.
On the other hand, feature fusion modules are designed to combine different modal of semantic features, which leverage the information from both inputs for better accuracy.
Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications.
On the one hand, the integrated classification model contains multiple classifiers, not only the general classifier but also a refinement classifier to distinguish the confusing categories.
In this paper, we investigate a novel deep-model reusing task.
Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications.
Replacing the background and simultaneously adjusting foreground objects is a challenging task in image editing.
We introduce Synscapes -- a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis.
Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.
#2 best model for Semantic Segmentation on PASCAL VOC 2012 test