Incremental Object Detection via Meta-Learning Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) DOI 10.1109/TPAMI.2021.3124133 Early access on IEEE Xplore:https://ieeexplore.ieee.org/document/9599446 arXiv paper:https://arxiv.org/abs/2003.08798 ...
Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation - Hi-FT/ERD
6.1.2 Applications in object detection Equipping computer systems with the ability to learn from few examples for object detection has strong practical significance. Inspired by meta learning, Kang (Kang et al., 2019) proposed a novel few-shot detection model. Since the model lacks the ability ...
Figure 1. We adapt this figure from the Focal Loss paper [9]. YOLOv3 runs significantly faster than other detection methods with comparable performance. Times from either an M40 or Titan X, they are basically the same GPU. 2.1. Bounding Box Prediction(边界框预测) ...
4 presents the performance Botnet Attack Detection with Incremental Online Learning 53 evaluation of this method on a publicly available dataset. Lastly, Sect. 6 sum- marizes the paper. 2 Auto-Associative Dense RNN Based Botnet Attack Detection with Online Incremental Training We now present the ...
RankModelDetection: Full (mAP@0.5)PaperCodeResultYearTags 1 TAM 42.9 Learning Task-Aware Language-Image Representation for Class-Incremental Object Detection 2024 2 CL-DETR (ours) 40.1 Continual Detection Transformer for Incremental Object Detection ...
In this work, we propose three incremental learning scenarios across various domains and categories for object detection. To mitigate catastrophic forgetting, attentive feature distillation is proposed to leverages both bottom-up and top-down attentions to extract important information for distillation. We...
Alternatively, the user can generate a C‐code scanner from the automaton. The generated automaton uses a direct execution approach and is quite fast.LexAGen is implemented in Smalltalk‐80. Its extensive use of interactive graphics makes it very easy to use. In addition, the object‐oriented ...
Class-incremental object detection (CIOD) is a real-world desired capability, requiring an object detector to continuously adapt to new tasks without forgetting learned ones, with the main challenge being catastrophic forgetting. Many methods based on distillation and replay have been proposed to allevi...
Our approach, DualFusion, combines object detectors in a manner that allows us to learn to detect rare objects with very limited data, all without severely degrading the performance of the detector on the abundant classes. In the IDD OpenSet incremental few-shot detection task, we achieve a ...