3 citations · 10 across the 17 of their papers we have counts for
17 papers
Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs
Zhenyu Liu, Xuanyu Zhang, Yunxin Li +10
Developing seamless, high-performance, native intelligent full-duplex Spoken Language Models (SLMs) remains a critical challenge and long-standing goal for the speech and NLP commu…
DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling
Tengyao Tu, Yulin Li, Hui-Ling Zhen +6
Recent advances in Large Reasoning Models (LRMs) demonstrate remarkable performance improvements by iteratively reflecting, exploring, and executing complex tasks, yet suffer from…
CoCoReviewBench: A Completeness- and Correctness-Oriented Benchmark for AI Reviewers
Hexuan Deng, Xiaopeng Ke, Yichen Li +6
Despite the rapid development of AI reviewers, evaluating such systems remains challenging: metrics favor overlap with human reviews over correctness. However, since human reviews…
MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation
Haoyuan Shi, Yunxin Li, Nanhao Deng +5
The evolution of video generation toward complex, multi-shot narratives has exposed a critical deficit in current evaluation methods. Existing benchmarks remain anchored to single-…
FutureOmni: Evaluating Future Forecasting from Omni-Modal Context for Multimodal LLMs
Qian Chen, Jinlan Fu, Changsong Li +3
Although Multimodal Large Language Models (MLLMs) demonstrate strong omni-modal perception, their ability to forecast future events from audio-visual cues remains largely unexplore…
Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data
Yunxin Li, Xinyu Chen, Shenyuan Jiang +9
We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal…
UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity MoE
Zhenyu Liu, Yunxin Li, Xuanyu Zhang +13
Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. However, the auditory domain remains a significant challenge, with mus…
VideoVista-CulturalLingo: 360 Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension
Xinyu Chen, Yunxin Li, Haoyuan Shi +4
Assessing the video comprehension capabilities of multimodal AI systems can effectively measure their understanding and reasoning abilities. Most video evaluation benchmarks are li…
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Yunxin Li, Zhenyu Liu, Zitao Li +19
Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasi…
Picking the Cream of the Crop: Visual-Centric Data Selection with Collaborative Agents
Zhenyu Liu, Yunxin Li, Baotian Hu +3
To improve Multimodal Large Language Models' (MLLMs) ability to process images and complex instructions, researchers predominantly curate large-scale visual instruction tuning data…
Anim-Director: A Large Multimodal Model Powered Agent for Controllable Animation Video Generation
Yunxin Li, Haoyuan Shi, Baotian Hu +5
Traditional animation generation methods depend on training generative models with human-labelled data, entailing a sophisticated multi-stage pipeline that demands substantial huma…
VideoVista: A Versatile Benchmark for Video Understanding and Reasoning
Yunxin Li, Xinyu Chen, Baotian Hu +3
Despite significant breakthroughs in video analysis driven by the rapid development of large multimodal models (LMMs), there remains a lack of a versatile evaluation benchmark to c…
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
Yunxin Li, Shenyuan Jiang, Baotian Hu +5
Recent advancements in Multimodal Large Language Models (MLLMs) underscore the significance of scalable models and data to boost performance, yet this often incurs substantial comp…
VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
Yunxin Li, Baotian Hu, Haoyuan Shi +3
Large Multimodal Models (LMMs) have achieved impressive success in visual understanding and reasoning, remarkably improving the performance of mathematical reasoning in a visual co…
A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation
Yunxin Li, Baotian Hu, Wenhan Luo +3
In this paper, we propose a new setting for generating product descriptions from images, augmented by marketing keywords. It leverages the combined power of visual and textual info…
Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment
Yunxin Li, Xinyu Chen, Baotian Hu +2
Evaluating and Rethinking the current landscape of Large Multimodal Models (LMMs), we observe that widely-used visual-language projection approaches (e.g., Q-former or MLP) focus o…
LLMs Meet Long Video: Advancing Long Video Question Answering with An Interactive Visual Adapter in LLMs
Yunxin Li, Xinyu Chen, Baotain Hu +1
Long video understanding is a significant and ongoing challenge in the intersection of multimedia and artificial intelligence. Employing large language models (LLMs) for comprehend…