Search Results for author:
Found 9 papers, 7 papers with code
NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets
We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results.
Ranked #1 on
Visual Object Tracking
on UAV123
Designing Network Design Strategies Through Gradient Path Analysis
This paper proposes a new network design strategy, i. e., to design the network architecture based on gradient path analysis.
SearchTrack: Multiple Object Tracking with Object-Customized Search and Motion-Aware Features
For each object, a customized fully convolutional search engine is created by SearchTrack by learning a set of weights for dynamic convolutions specific to the object.
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56. 8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100.
Ranked #3 on
Real-Time Object Detection
on COCO
(using extra training data)
You Only Learn One Representation: Unified Network for Multiple Tasks
In this paper, we propose a unified network to encode implicit knowledge and explicit knowledge together, just like the human brain can learn knowledge from normal learning as well as subconsciousness learning.
Ranked #1 on
Real-Time Object Detection
on COCO test-dev
Scaled-YOLOv4: Scaling Cross Stage Partial Network
We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy.
Ranked #9 on
Object Detection
on COCO test-dev
YOLOv4: Optimal Speed and Accuracy of Object Detection
There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy.
Ranked #30 on
Object Detection
on COCO test-dev
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
Ranked #591 on
Image Classification
on ImageNet
Residual Bi-Fusion Feature Pyramid Network for Accurate Single-shot Object Detection
A new structure "residual feature pyramid" is proposed in this paper.
