Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation-Reference-Cited by-同舟云学术

Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
Short-container-title:

Author:

Jung Sanghun¹,Lee Jungsoo¹,Gwak Daehoon¹,Choi Sungha²,Choo Jaegul¹

Affiliation:

1. KAIST AI

2. LG AI Research

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09711261.pdf?arnumber=9711261

Reference37 articles.

1. Synthesize then compare: Detecting failures and anomalies for semantic segmentation;xia;Proc Of the European Conference on Computer Vision (ECCV),2020

2. Active boundary loss for semantic segmentation;wang,2021

3. MobileNetV2: Inverted Residuals and Linear Bottlenecks

4. ImageNet Large Scale Visual Recognition Challenge

5. Asymmetric Non-Local Neural Networks for Semantic Segmentation

Cited by 50 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Local context attention learning for fine-grained scene graph generation;Pattern Recognition;2024-12

2. Hybrid Open-Set Segmentation With Synthetic Negative Data;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-10

3. Advancements in urban scene segmentation using deep learning and generative adversarial networks for accurate satellite image analysis;PLOS ONE;2024-07-18

4. A Metacognitive Approach to Out-of-Distribution Detection for Segmentation;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

5. Road Obstacle Detection based on Unknown Objectness Scores;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13