C-COT: Continuous Convolution Operators for Visual Tracking 论文学习 - 代码天地

C-COT: Continuous Convolution Operators for Visual Tracking 论文学习

其他 2019-03-29 16:20:49 阅读次数: 0

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转载自blog.csdn.net/calvinpaean/article/details/85000191

C-COT: Continuous Convolution Operators for Visual Tracking 论文学习

C-COT：Learning Continuous Convolution Operators for Visual Tracking程序问题

C-COT -- 超越相关滤波：学习连续卷积算子(Continuous Convolution Operators)

ECO: Efficient Convolution Operators for Tracking 论文阅读

ECO(Efficient Convolution Operators for Tracking)解析

ECO: Efficient Convolution Operators for Tracking视频目标跟踪论文笔记（PPT版）

【目标跟踪】ECO算法论文阅读：ECO: Efficient Convolution Operators for Tracking

【论文学习】One-shot Adversarial Attacks on Visual Tracking with Dual Attention论文学习

High Performance Visual Tracking with Siamese Region Proposal Network 论文学习

SiamRPN++：Evolution of Siamese Visual Tracking with very Deep Netwrks 论文学习

Distractor-aware Siamese Networks for Visual Object Tracking 论文学习

SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines 论文学习

Siam R-CNN: Visual Tracking by Re-Detection 论文学习

Operators in MXNet-Convolution

论文笔记：Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning

C Operators

2018年CVPR中部分目标跟踪论文visual tracking

论文《Learning regression and verification networks for long-term visual tracking》

论文笔记：Learning Attribute-Specific Representations for Visual Tracking

动态SLAM论文(7) — DOT: Dynamic Object Tracking for Visual SLAM

论文解读--Visual Lane Tracking and Prediction for Autonomous Vehicles

【论文学习记录】Rethinking Atrous Convolution for Semantic Image Segmentation

Particle filter for visual tracking

Fast Online Object Tracking and Segmentation: A Unifying Approach 论文学习

Fully-Convolutional Siamese Networks for Object Tracking 论文学习

Understanding and Diagnosing Visual Tracking Systems

Visual Object Tracking: MDNet + VITAL

Recurrent Filter Learning for Visual Tracking

【论文学习】Fooling Detection Alone is Not Enough--Adversarial Attack against Multiple Object Tracking论文学习

【论文笔记】Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition

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