AI 中文总结
研究增量目标检测问题，提出共生启发的知识蒸馏方法（SIKD），通过空间共生蒸馏和语义共生蒸馏在两个层面利用对象共生，有效解决现有方法不足，实验验证了该方法的有效性和优越性。
AI 中文摘要
增量目标检测旨在扩展检测器以处理新类别，同时保留先前知识。现有方法常采用类增量学习视角，分离特征空间。但这种范式忽视检测中的对象共生，共生会引入空间和语义依赖，忽略这些会扭曲共享表示等问题。为此提出共生启发的知识蒸馏（SIKD），它在两个互补层面利用对象共生。空间共生蒸馏（SpSD）关注旧模型与新任务对象高重叠的共生区域，保留旧类线索等并在匹配空间位置蒸馏证据。语义共生蒸馏（SeSD）通过为旧类形成置信加权原型等维持类级结构。实验证明了该方法的有效性和优越性。
英文摘要
Incremental object detection (IOD) aims to extend detectors to new categories while retaining previously acquired knowledge. Existing methods often adopt a class incremental learning perspective, separating feature spaces to sharpen decision boundaries. However, this separation-oriented paradigm may overlook object symbiosis in detection, where co-occurrence and occlusion introduce spatial and semantic dependencies that benefit from shared representations. Ignoring these dependencies distorts the shared representations, exacerbates confusion between old and new classes, and accelerates catastrophic forgetting. To address this, we propose Symbiosis-Inspired Knowledge Distillation (SIKD), which explicitly leverages object symbiosis at two complementary levels. Spatial Symbiosis Distillation (SpSD) focuses on symbiotic regions where the old model responds with high overlap to objects in the new task. It preserves generalizable old class cues, suppresses class-specific bias and redundancy, and distills the refined evidence to the new model at matched spatial locations with slot-aligned supervision. Semantic Symbiosis Distillation (SeSD) maintains class level structure by forming confidence weighted prototypes for old classes and aligning their inter class soft ranks over the old class logits, which stabilizes the semantic topology during adaptation. Extensive experiments demonstrate the effectiveness and superiority of the proposed method.
Comments16 pages, 8 figures, Accepted by ICML 2026
