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cs.CV2026

From Glance to Scrutiny: Progressive Distortion Reasoning for Fine-Grained Image Quality Assessment

Aoting Zhang, Mingze Gao, Dongbao Yang +5

Multi-modal large language models (MLLMs) have demonstrated significant potential in image quality assessment (IQA) by bridging visual perception with descriptive evaluations. Howe…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…