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Found 5 papers, 3 papers with code
Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations
We show that adding a consistency loss, ensuring that the predictions of the network are consistent over consecutive training epochs, is a simple yet effective method to train multi-label classifiers in a weakly supervised setting.
BlockCopy: High-Resolution Video Processing with Block-Sparse Feature Propagation and Online Policies
In this paper we propose BlockCopy, a scheme that accelerates pretrained frame-based CNNs to process video more efficiently, compared to standard frame-by-frame processing.
SegBlocks: Block-Based Dynamic Resolution Networks for Real-Time Segmentation
For instance, our method reduces the number of floating-point operations of SwiftNet-RN18 by 60% and increases the inference speed by 50%, with only 0. 3% decrease in mIoU accuracy on Cityscapes.
Ranked #6 on
Semantic Segmentation
on Mapillary val
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference
Modern convolutional neural networks apply the same operations on every pixel in an image.
Generating superpixels using deep image representations
Superpixel algorithms are a common pre-processing step for computer vision algorithms such as segmentation, object tracking and localization.
