15 citations · 27 across the 14 of their papers we have counts for
16 papers
DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
Jiarui Feng, Hanqing Zeng, Karish Grover +11
Mixture-of-Experts (MoE) models have become a leading approach for decoupling parameter count from computational cost in large language models, yet effectively scaling MoE performa…
ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment
Zhipeng Bian, Jieming Zhu, Qijiong Liu +6
Recent advances in multimodal large language models (MLLMs) and diffusion models (DMs) have opened new possibilities for AI-generated content. Yet, personalized cover image generat…
MIRA: Empowering One-Touch AI Services on Smartphones with MLLM-based Instruction Recommendation
Zhipeng Bian, Jieming Zhu, Xuyang Xie +3
The rapid advancement of generative AI technologies is driving the integration of diverse AI-powered services into smartphones, transforming how users interact with their devices.…
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…
EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
Minjie Hong, Yan Xia, Zehan Wang +8
Large language models (LLMs) are increasingly leveraged as foundational backbones in the development of advanced recommender systems, offering enhanced capabilities through their e…
Learning Multi-Aspect Item Palette: A Semantic Tokenization Framework for Generative Recommendation
Qijiong Liu, Jieming Zhu, Zhaocheng Du +3
Traditional recommendation models often rely on unique item identifiers (IDs) to distinguish between items, which can hinder their ability to effectively leverage item content info…
EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration
Ye Wang, Jiahao Xun, Minjie Hong +8
Generative retrieval has recently emerged as a promising approach to sequential recommendation, framing candidate item retrieval as an autoregressive sequence generation problem. H…
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…
Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey
Qijiong Liu, Jieming Zhu, Yanting Yang +6
Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests. However, traditional recommendation models primarily rely…
Enhancing Multimodal Unified Representations for Cross Modal Generalization
Hai Huang, Yan Xia, Shengpeng Ji +7
To enhance the interpretability of multimodal unified representations, many studies have focused on discrete unified representations. These efforts typically start with contrastive…
Multi-Modal Domain Adaptation Across Video Scenes for Temporal Video Grounding
Haifeng Huang, Yang Zhao, Zehan Wang +2
Temporal Video Grounding (TVG) aims to localize the temporal boundary of a specific segment in an untrimmed video based on a given language query. Since datasets in this domain are…
Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks
Haoyi Duan, Yan Xia, Mingze Zhou +3
In recent years, the deployment of large-scale pre-trained models in audio-visual downstream tasks has yielded remarkable outcomes. However, these models, primarily trained on sing…
MART: Learning Hierarchical Music Audio Representations with Part-Whole Transformer
Dong Yao, Jieming Zhu, Jiahao Xun +6
Recent research in self-supervised contrastive learning of music representations has demonstrated remarkable results across diverse downstream tasks. However, a prevailing trend in…