Oriented R-CNN With Disentangled Representations for Product Packaging Detection
Object detection is a vital task in the field of computer vision for various applications such as face detection, autonomous driving and industrial production. In recent years, with the rise of deep neural networks, there has been significant progress in improving object detection accuracy. However,...
Saved in:
Main Authors: | Jiangyi Pan, Jianjun Yang, Yinhao Liu, Yijie Lv |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Photonics Journal |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10648834/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
UAV Collision Avoidance in Unknown Scenarios with Causal Representation Disentanglement
by: Zhun Fan, et al.
Published: (2024-12-01) -
Mapping Mental Representations With Free Associations: A Tutorial Using the R Package associatoR
by: Samuel Aeschbach, et al.
Published: (2025-01-01) -
Efficient and Fast Light Field Compression via VAE-Based Spatial and Angular Disentanglement
by: Soheib Takhtardeshir, et al.
Published: (2025-01-01) -
Dual-stream disentangled model for microvascular extraction in five datasets from multiple OCTA instruments
by: Xiaoyang Hu, et al.
Published: (2025-01-01) -
Disentangled Contrastive Learning From Synthetic Matching Pairs for Targeted Chest X-Ray Generation
by: Euyoung Kim, et al.
Published: (2025-01-01)