3 accepted papers
Scene Graph Generation (SGG) is pivotal for structured visual understanding, yet it remains hindered by a fundamental limitation: the reliance on fixed, frozen semantic representations from pre-trained language models. These semantic priors, while beneficial in other domains, are inherently misalign…
Vision-language models, such as CLIP, achieve strong zero-shot performance through contrastive pre-training but face significant challenges in class-incremental image classification scenarios. When learning new tasks sequentially, current methods suffer from degradation in prototype quality due to p…
The continuous emergence of new entities, relations, triples, and multimodal information drives the dynamic evolution of multimodal knowledge graph (MMKG). However, existing MMKG embedding models follow a static setting, where training from scratch for growing MMKG wastes learned knowledge, while fi