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We develop a new edge detection algorithm that tackles two important issues in this long-standing vision problem: (1) holistic image training and prediction; and (2) multi-scale and multi-level feature learning.
#3 best model for Edge Detection on BIPED
Apart from these, several image manipulation techniques using these plugins have been compiled and demonstrated in the YouTube playlist (https://www. youtube. com/playlist? list=PLo9r5wFmpD5dLWTyo6NOiD6BJjhfEOM5t) with the objective of demonstrating the use-cases for machine learning based image modification.
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.
#2 best model for Human Part Segmentation on CIHP
Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.
In this work, we propose a novel dynamic feature fusion strategy that assigns different fusion weights for different input images and locations adaptively.
To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features.
SOTA for Edge Detection on SBD
To demonstrate the superiority and generality of the proposed method, we evaluate the proposed method on five crack datasets and compare it with state-of-the-art crack detection, edge detection, semantic segmentation methods.