12 citations · 47 across the 32 of their papers we have counts for
32 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…
KnowAct-GUIClaw: Know Deeply, Act Perfectly, Personal GUI Assistant with Self-Evolving Memory and Skill
Yunxin Li, Jinchao Li, Shibo Su +7
OpenClaw has emerged as a leading agent framework for complex task automation, yet it faces insufficient cross-platform GUI interaction support and a well-built self-evolution mech…
Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations
Xinxin Li, Huiyao Chen, Meishan Zhang +6
Automatic speech recognition (ASR) correction has traditionally focused on isolated utterances or short local contexts. However, as text and speech become increasingly interleaved…
WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments
Jinchao Li, Yunxin Li, Chenrui Zhao +3
While GUI agents have shown impressive capabilities in common computer-use tasks such as OSWorld, current benchmarks mainly focus on isolated and single-application tasks. This ove…
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-…
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…
A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation
Ziyang Chen, Erxue Min, Xiang Zhao +7
We introduce ChronoQA, a large-scale benchmark dataset for Chinese question answering, specifically designed to evaluate temporal reasoning in Retrieval-Augmented Generation (RAG)…
AniMaker: Multi-Agent Animated Storytelling with MCTS-Driven Clip Generation
Haoyuan Shi, Yunxin Li, Xinyu Chen +3
Despite rapid advancements in video generation models, generating coherent storytelling videos that span multiple scenes and characters remains challenging. Current methods often r…
VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy Optimization
Yunxin Li, Xinyu Chen, Zitao Li +5
Applying Reinforcement Learning (RL) to Video Large Language Models (Video-LLMs) shows significant promise for complex video reasoning. However, popular Reinforcement Fine-Tuning (…
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…
UI-TARS: Pioneering Automated GUI Interaction with Native Agents
Yujia Qin, Yining Ye, Junjie Fang +32
This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e.g., keyboard and mouse operations). U…
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…
Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs
Yunxin Li, Zhenyu Liu, Baotian Hu +4
Recent advancements in multimodal large language models (MLLMs) have achieved significant multimodal generation capabilities, akin to GPT-4. These models predominantly map visual i…
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering
Yunxin Li, Longyue Wang, Baotian Hu +5
The emergence of multimodal large models (MLMs) has significantly advanced the field of visual understanding, offering remarkable capabilities in the realm of visual question answe…
Training Multimedia Event Extraction With Generated Images and Captions
Zilin Du, Yunxin Li, Xu Guo +2
Contemporary news reporting increasingly features multimedia content, motivating research on multimedia event extraction. However, the task lacks annotated multimodal training data…