21 citations · 23 across the 14 of their papers we have counts for
15 papers
F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting
Jiayi Zhang, Jinfeng Xu, Hewei Wang +7
Spatiotemporal prediction on graph-structured data is central to traffic forecasting and environmental monitoring, yet decentralized and heterogeneous data complicate both sequence…
Building a Precise Video Language with Human-AI Oversight
Zhiqiu Lin, Chancharik Mitra, Siyuan Cen +13
Video-language models (VLMs) learn to reason about the dynamic visual world through natural language. We introduce a suite of open datasets, benchmarks, and recipes for scalable ov…
Well Begun is Half Done: Training-Free and Model-Agnostic Semantically Guaranteed User Representation Initialization for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
Recent advancements in multimodal recommendations, which leverage diverse modality information to mitigate data sparsity and improve recommendation accuracy, have gained significan…
CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
The explosion of multimedia data in information-rich environments has intensified the challenges of personalized content discovery, positioning recommendation systems as an essenti…
Learning and Editing Universal Graph Prompt Tuning via Reinforcement Learning
Jinfeng Xu, Zheyu Chen, Shuo Yang +4
Early graph prompt tuning approaches relied on task-specific designs for Graph Neural Networks (GNNs), limiting their adaptability across diverse pre-training strategies. In contra…
VI-MMRec: Similarity-Aware Training Cost-free Virtual User-Item Interactions for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
Although existing multimodal recommendation models have shown promising performance, their effectiveness continues to be limited by the pervasive data sparsity problem. This proble…
Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
Multiple clustering aims to discover diverse latent structures from different perspectives, yet existing methods generate exhaustive clusterings without discerning user interest, n…
Spec-LLaVA: Accelerating Vision-Language Models with Dynamic Tree-Based Speculative Decoding
Mingxiao Huo, Jiayi Zhang, Hewei Wang +4
Vision-Language Models (VLMs) enable powerful multimodal reasoning but suffer from slow autoregressive inference, limiting their deployment in real-time applications. We introduce…
Hypercomplex Prompt-aware Multimodal Recommendation
Zheyu Chen, Jinfeng Xu, Hewei Wang +3
Modern recommender systems face critical challenges in handling information overload while addressing the inherent limitations of multimodal representation learning. Existing metho…
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +5
Graph Neural Networks (GNNs) are widely used in collaborative filtering to capture high-order user-item relationships. To address the data sparsity problem in recommendation system…
Consistent Video Editing as Flow-Driven Image-to-Video Generation
Ge Wang, Songlin Fan, Hangxu Liu +3
With the prosper of video diffusion models, down-stream applications like video editing have been significantly promoted without consuming much computational cost. One particular c…
MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets
Jinfeng Xu, Zheyu Chen, Jinze Li +6
The data sparsity problem significantly hinders the performance of recommender systems, as traditional models rely on limited historical interactions to learn user preferences and…
Multi-Cali Anything: Dense Feature Multi-Frame Structure-from-Motion for Large-Scale Camera Array Calibration
Jinjiang You, Hewei Wang, Yijie Li +8
Calibrating large-scale camera arrays, such as those in dome-based setups, is time-intensive and typically requires dedicated captures of known patterns. While extrinsics in such a…
AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation
Jinfeng Xu, Zheyu Chen, Jinze Li +3
Group activities are important behaviors in human society, providing personalized recommendations for groups is referred to as the group recommendation task. Existing methods can u…
MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +3
With the increasing multimedia information, multimodal recommendation has received extensive attention. It utilizes multimodal information to alleviate the data sparsity problem in…