62 citations · 132 across the 14 of their papers we have counts for
16 papers
Omni-R1: Towards the Unified Generative Paradigm for Multimodal Reasoning
Dongjie Cheng, Yongqi Li, Zhixin Ma +5
Multimodal Large Language Models (MLLMs) are making significant progress in multimodal reasoning. Early approaches focus on pure text-based reasoning. More recent studies have inco…
TTOM: Test-Time Optimization and Memorization for Compositional Video Generation
Leigang Qu, Ziyang Wang, Na Zheng +3
Video Foundation Models (VFMs) exhibit remarkable visual generation performance, but struggle in compositional scenarios (e.g., motion, numeracy, and spatial relation). In this wor…
Parallel Test-Time Scaling for Latent Reasoning Models
Runyang You, Yongqi Li, Meng Liu +3
Parallel test-time scaling (TTS) is a pivotal approach for enhancing large language models (LLMs), typically by sampling multiple token-based chains-of-thought in parallel and aggr…
Rec: Towards Large Recommender Models with Reasoning
Runyang You, Yongqi Li, Xinyu Lin +4
Large recommender models have extended LLMs as powerful recommenders via encoding or item generation, and recent breakthroughs in LLM reasoning synchronously motivate the explorati…
Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation
Han Liu, Yinwei Wei, Fan Liu +3
Multimodal information (e.g., visual, acoustic, and textual) has been widely used to enhance representation learning for micro-video recommendation. For integrating multimodal info…
SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation
Leigang Qu, Haochuan Li, Wenjie Wang +4
Large Multimodal Models (LMMs) have demonstrated impressive capabilities in multimodal understanding and generation, pushing forward advancements in text-to-image generation. Howev…
Revolutionizing Text-to-Image Retrieval as Autoregressive Token-to-Voken Generation
Yongqi Li, Hongru Cai, Wenjie Wang +5
Text-to-image retrieval is a fundamental task in multimedia processing, aiming to retrieve semantically relevant cross-modal content. Traditional studies have typically approached…
TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models
Leigang Qu, Haochuan Li, Tan Wang +4
How humans can effectively and efficiently acquire images has always been a perennial question. A classic solution is text-to-image retrieval from an existing database; however, th…
Discriminative Probing and Tuning for Text-to-Image Generation
Leigang Qu, Wenjie Wang, Yongqi Li +3
Despite advancements in text-to-image generation (T2I), prior methods often face text-image misalignment problems such as relation confusion in generated images. Existing solutions…
Generative Cross-Modal Retrieval: Memorizing Images in Multimodal Language Models for Retrieval and Beyond
Yongqi Li, Wenjie Wang, Leigang Qu +3
The recent advancements in generative language models have demonstrated their ability to memorize knowledge from documents and recall knowledge to respond to user queries effective…
Distillation Enhanced Generative Retrieval
Yongqi Li, Zhen Zhang, Wenjie Wang +3
Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful…
A Federated Framework for LLM-based Recommendation
Jujia Zhao, Wenjie Wang, Chen Xu +2
Large Language Models (LLMs) have empowered generative recommendation systems through fine-tuning user behavior data. However, utilizing the user data may pose significant privacy…
GOODAT: Towards Test-time Graph Out-of-Distribution Detection
Luzhi Wang, Dongxiao He, He Zhang +5
Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. While GNNs excel in scenarios where the testing data shares the distri…
General Debiasing for Multimodal Sentiment Analysis
Teng Sun, Juntong Ni, Wenjie Wang +3
Existing work on Multimodal Sentiment Analysis (MSA) utilizes multimodal information for prediction yet unavoidably suffers from fitting the spurious correlations between multimoda…
Learnable Pillar-based Re-ranking for Image-Text Retrieval
Leigang Qu, Meng Liu, Wenjie Wang +3
Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities. Prior work usually focuses on the pairwise relations (i.e., wh…
Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis
Teng Sun, Wenjie Wang, Liqiang Jing +3
Existing studies on multimodal sentiment analysis heavily rely on textual modality and unavoidably induce the spurious correlations between textual words and sentiment labels. This…