Benchmarks
Greatest papers with code
TernausNetV2: Fully Convolutional Network for Instance Segmentation
The most common approaches to instance segmentation are complex and use two-stage networks with object proposals, conditional random-fields, template matching or recurrent neural networks.
INSTANCE SEGMENTATION SEMANTIC SEGMENTATION TEMPLATE MATCHING TRANSFER LEARNING
Deep Watershed Transform for Instance Segmentation
CVPR 2017
• min2209/dwt
• 
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes.
Ranked #8 on
Instance Segmentation
on Cityscapes test
INSTANCE SEGMENTATION SEMANTIC SEGMENTATION TEMPLATE MATCHING
Learning Deep Features for One-Class Classification
We propose a deep learning-based solution for the problem of feature learning in one-class classification.
Tracking Holistic Object Representations
• xl-sr/THOR
• 
The framework leverages the idea of obtaining additional object templates during the tracking process.
Ranked #2 on
Visual Object Tracking
on VOT2017/18
BOP: Benchmark for 6D Object Pose Estimation
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image.
6D POSE ESTIMATION 6D POSE ESTIMATION USING RGB 6D POSE ESTIMATION USING RGBD TEMPLATE MATCHING
Fast and accurate spike sorting of high-channel count probes with KiloSort
Unlike previous algorithms that compress the data with PCA, KiloSort operates on the raw data which allows it to construct a more accurate model of the waveforms.
QATM: Quality-Aware Template Matching For Deep Learning
CVPR 2019
• cplusx/QATM
•
Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc.
Template Matching with Deformable Diversity Similarity
We propose a novel measure for template matching named Deformable Diversity Similarity -- based on the diversity of feature matches between a target image window and the template.
Learning Dynamic Memory Networks for Object Tracking
ECCV 2018
• skyoung/MemTrack
•
In this paper, we propose a dynamic memory network to adapt the template to the target's appearance variations during tracking.
GradNet: Gradient-Guided Network for Visual Object Tracking
ICCV 2019
• LPXTT/GradNet-Tensorflow
•
In this work, we propose a novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations.
Ranked #2 on
Visual Object Tracking
on OTB-2015
(Precision metric)