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Found 6 papers, 6 papers with code
Referenceless User Controllable Semantic Image Synthesis
Despite recent progress in semantic image synthesis, complete control over image style remains a challenging problem.
SuperStyleNet: Deep Image Synthesis with Superpixel Based Style Encoder
First, we directly extract the style codes from the original image based on superpixels to consider local objects.
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation
By integrating the SGC and GPA together, we propose the Adaptive Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to object scale and shape variation.
Ranked #63 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Edge and Identity Preserving Network for Face Super-Resolution
In this paper, we propose a novel Edge and Identity Preserving Network for Face SR Network, named as EIPNet, to minimize the distortion by utilizing a lightweight edge block and identity information.
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation
As a pixel-level prediction task, semantic segmentation needs large computational cost with enormous parameters to obtain high performance.
Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment
Pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, and there exists the proposal shift problem in pedestrian detection that causes the loss of body parts such as head and legs.
Ranked #27 on
Pedestrian Detection
on Caltech
