7 papers
CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling
Minghui Fang, Shengpeng Ji, Jialong Zuo +9
Cross-modal retrieval aims to search for instances, which are semantically related to the query through the interaction of different modal data. Traditional solutions utilize a sin…
RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation
Sashuai Zhou, Weinan Gan, Qijiong Liu +7
Recent advances in LLM-based recommendation have shown promise, yet their cross-domain generalization is hindered by a fundamental mismatch between language-centric pretraining and…
Open-set Cross Modal Generalization via Multimodal Unified Representation
Hai Huang, Yan Xia, Shulei Wang +6
This paper extends Cross Modal Generalization (CMG) to open-set environments by proposing the more challenging Open-set Cross Modal Generalization (OSCMG) task. This task evaluates…
Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations
Hai Huang, Yan Xia, Sashuai Zhou +3
Domain Generalization (DG) aims to enhance model robustness in unseen or distributionally shifted target domains through training exclusively on source domains. Although existing D…
Continual Cross-Modal Generalization
Yan Xia, Hai Huang, Minghui Fang +1
Cross-modal generalization aims to learn a shared discrete representation space from multimodal pairs, enabling knowledge transfer across unannotated modalities. However, achieving…
Enhancing Multi-modal Models with Heterogeneous MoE Adapters for Fine-tuning
Sashuai Zhou, Hai Huang, Yan Xia
Multi-modal models excel in cross-modal tasks but are computationally expensive due to their billions of parameters. Parameter-efficient fine-tuning (PEFT) offers a solution by add…