# Efficient Parameter Mining and Freezing for Continual Object Detection

Source: [Efficient Parameter Mining and Freezing for Continual Object Detection](<https://www.sota2.com/research/paper/68fa72451e209131cea5574b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:22:12.513Z

Authors: Angelo G. Menezes, Augusto J. Peterlevitz, Mateus A. Chinelatto, Andr\\'e C. P. L. F. de Carvalho

Published: 2024-02-20T01:07:32.000Z

## Abstract

Continual Object Detection is essential for enabling intelligent agents to interact proactively with humans in real-world settings. While parameter-isolation strategies have been extensively explored in the context of continual learning for classification, they have yet to be fully harnessed for incremental object detection scenarios. Drawing inspiration from prior research that focused on mining individual neuron responses and integrating insights from recent developments in neural pruning, we proposed efficient ways to identify which layers are the most important for a network to maintain the performance of a detector across sequential updates. The presented findings highlight the substantial advantages of layer-level parameter isolation in facilitating incremental learning within object detection models, offering promising avenues for future research and application in real-world scenarios.

## 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](<https://arxiv.org/abs/2402.12624>)

[PDF](<https://arxiv.org/pdf/2402.12624.pdf>)