Search Results for author:
Found 4 papers, 1 papers with code
Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes
It is an inference-based method that does not require training on the domain dataset and relies on extracting relevant features from self-supervised pre-trained models.
Spiking CenterNet: A Distillation-boosted Spiking Neural Network for Object Detection
To the best of our knowledge, our work is the first approach that takes advantage of knowledge distillation in the field of spiking object detection.
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications.
From Black-box to White-box: Examining Confidence Calibration under different Conditions
Based on previous work, we study the miscalibration of object detection models with respect to image location and box scale.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
