# Hierarchical Neural Collapse Detection Transformer for Class Incremental Object Detection Source: [Hierarchical Neural Collapse Detection Transformer for Class Incremental Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:54:52.375Z Authors: Duc Thanh Pham, Hong Dang Nguyen, Nhat Minh Nguyen Quoc, Linh Ngo Van, Sang Dinh Viet, Duc Anh Nguyen Published: 2025-06-10T08:32:28.000Z ## Abstract Recently, object detection models have witnessed notable performance improvements, particularly with transformer-based models. However, new objects frequently appear in the real world, requiring detection models to continually learn without suffering from catastrophic forgetting. Although Incremental Object Detection (IOD) has emerged to address this challenge, these existing models are still not practical due to their limited performance and prolonged inference time. In this paper, we introduce a novel framework for IOD, called Hier-DETR: Hierarchical Neural Collapse Detection Transformer, ensuring both efficiency and competitive performance by leveraging Neural Collapse for imbalance dataset and Hierarchical relation of classes' labels. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()