5 papers
Knowing Beyond the Known: Reinforced Knowledge Specification for Multi-Label Class-Incremental Learning
Aoting Zhang, Dongbao Yang, Chang Liu +3
Existing class-incremental learning methods struggle in multi-label scenarios (MLCIL) due to the inherent contradiction of learning objectives arising from co-occurring and incompl…
Orthogonal Knowledge Refreshing for Domain-Incremental Object Detection
Aoting Zhang, Dongbao Yang, Chang Liu +3
Domain-incremental object detection (DIOD) requires models to continually adapt to new domains while preserving prior knowledge. Recently, parameter-efficient fine-tuning offers a…
Focus, Align, and Sustain: Counteracting Gradient Dilution in Incremental Object Detection
Aoting Zhang, Dongbao Yang, Chang Liu +2
Adapting Detection Transformers to Incremental Object Detection (IOD) poses a systemic challenge, as set-based optimization is inherently destabilized by sequential learning. In th…
Specifying What You Know or Not for Multi-Label Class-Incremental Learning
Aoting Zhang, Dongbao Yang, Chang Liu +2
Existing class incremental learning is mainly designed for single-label classification task, which is ill-equipped for multi-label scenarios due to the inherent contradiction of le…
DCA: Dividing and Conquering Amnesia in Incremental Object Detection
Aoting Zhang, Dongbao Yang, Chang Liu +3
Incremental object detection (IOD) aims to cultivate an object detector that can continuously localize and recognize novel classes while preserving its performance on previous clas…