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Found 9 papers, 5 papers with code
Spatial Pyramid Based Graph Reasoning for Semantic Segmentation
The convolution operation suffers from a limited receptive filed, while global modeling is fundamental to dense prediction tasks, such as semantic segmentation.
MFPN: A Novel Mixture Feature Pyramid Network of Multiple Architectures for Object Detection
Feature pyramids are widely exploited in many detectors to solve the scale variation problem for object detection.
SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
In order to overcome the lack of supervision, we introduce a differentiable module to resolve the overlap between any pair of instances.
Ranked #8 on
Panoptic Segmentation
on Cityscapes test
CBNet: A Novel Composite Backbone Network Architecture for Object Detection
In existing CNN based detectors, the backbone network is a very important component for basic feature extraction, and the performance of the detectors highly depends on it.
Ranked #43 on
Instance Segmentation
on COCO test-dev
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.
Ranked #145 on
Object Detection
on COCO test-dev
Deep Dual Pyramid Network for Barcode Segmentation using Barcode-30k Database
Specifically, a synthesizing method was proposed to generate well-annotated images containing barcode and QR code labels, which contributes to largely decrease the annotation time.
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition
In this challenge, we propose spatial-temporal network (StNet) for better joint spatial-temporal modelling and comprehensively video understanding.
CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving
The ability to detect small objects and the speed of the object detector are very important for the application of autonomous driving, and in this paper, we propose an effective yet efficient one-stage detector, which gained the second place in the Road Object Detection competition of CVPR2018 workshop - Workshop of Autonomous Driving(WAD).
