# CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection Source: [CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:25:31.619Z Authors: Mingyi Guo, Yuyang Liu, Zhiyuan Yan, Zongying Lin, Peixi Peng, Yonghong Tian Published: 2024-10-08T08:36:12.000Z ## Abstract Incremental object detection is fundamentally challenged by catastrophic forgetting. A major factor contributing to this issue is background shift, where background categories in sequential tasks may overlap with either previously learned or future unseen classes. To address this, we propose a novel method called Class-Agnostic Shared Attribute Base (CASA) that encourages the model to learn category-agnostic attributes shared across incremental classes. Our approach leverages an LLM to generate candidate textual attributes, selects the most relevant ones based on the current training data, and records their importance in an assignment matrix. For subsequent tasks, the retained attributes are frozen, and new attributes are selected from the remaining candidates, ensuring both knowledge retention and adaptability. Extensive experiments on the COCO dataset demonstrate the state-of-the-art performance of our method. ## 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]()