Different Changes Require Different Reasoning: Change-Type-Specialized Experts for Robust Change Captioning

Change captioning is the task of generating natural languagedescriptions that explain the changes between a pair of images. Althoughdifferent change types (e.g., color shifts, object additions) exhibit distinctvisual cues and require specialized reasoning processes, existing methodsoften overlook these distinctions. To address this limitation, we proposeMulti-Expert Diagnosis for Image Change (MEDIC), a novel frameworkthat introduces change-type awareness by explicitly modeling changecategories. We build our MEDIC as a memory network to dynamicallyretrieve type-relevant visual patterns conditioned on the input. This de-sign allows each expert to flexibly capture diverse variations within eachchange type and focus on the most informative cues for its designatedchange type. By routing inputs through type-specialized experts andlearning dedicated representations for each change category, MEDICgenerates more precise and type-aware change descriptions. Extensiveexperiments demonstrate that proposed MEDIC consistently outper-forms across diverse and challenging datasets. The code is available athttps://github.com/VisualAIKHU/MEDIC.