In this work, we revisit the global average pooling layer proposed in , and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels.
We develop an algorithm for the nontrivial end-to-end training of this causal, cascaded structure.
#3 best model for Multi-Human Parsing on PASCAL-Person-Part
Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information.
In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning.
#21 best model for Image Super-Resolution on Set5 - 4x upscaling
Our algorithm pretrains a CNN using a large set of videos with tracking ground-truths to obtain a generic target representation.
Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks.