12 citations · 14 across the 9 of their papers we have counts for
9 papers
MSUE: Multi-Modal Soccer Understanding Expert
Litao Li, Yibo Yu, Yufeng Hu +4
This paper presents our solution to the 2026 SoccerNet VQA Challenge. We first develop a cost-effective data synthesis pipeline driven by a Vision-Language Model (VLM), which syste…
Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLM
Tianyi Wu, Xiaoxi Sun, Yanhua Jiao +5
Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirectional attention mechanisms.…
ReFilter: Improving Robustness of Retrieval-Augmented Generation via Gated Filter
Yixin Chen, Ying Xiong, Shangyu Wu +3
Retrieval-augmented generation (RAG) has become a dominant paradigm for grounding large language models (LLMs) with external evidence in knowledge-intensive question answering. A c…
Multi-Agent Evolve: LLM Self-Improve through Co-evolution
Yixing Chen, Yiding Wang, Siqi Zhu +5
Reinforcement Learning (RL) has demonstrated significant potential in enhancing the reasoning capabilities of large language models (LLMs). However, the success of RL for LLMs heav…
A Comprehensive Evaluation framework of Alignment Techniques for LLMs
Muneeza Azmat, Momin Abbas, Maysa Malfiza Garcia de Macedo +9
As Large Language Models (LLMs) become increasingly integrated into real-world applications, ensuring their outputs align with human values and safety standards has become critical…
Visual Instruction Tuning with Chain of Region-of-Interest
Yixin Chen, Shuai Zhang, Boran Han +1
High-resolution (HR) images are pivotal for enhancing the recognition and understanding capabilities of multimodal large language models (MLLMs). However, directly increasing image…
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Yanwei Li, Yuechen Zhang, Chengyao Wang +5
In this work, we introduce Mini-Gemini, a simple and effective framework enhancing multi-modality Vision Language Models (VLMs). Despite the advancements in VLMs facilitating basic…
CaMML: Context-Aware Multimodal Learner for Large Models
Yixin Chen, Shuai Zhang, Boran Han +2
In this work, we introduce Context-Aware MultiModal Learner (CaMML), for tuning large multimodal models (LMMs). CaMML, a lightweight module, is crafted to seamlessly integrate mult…
Lightweight In-Context Tuning for Multimodal Unified Models
Yixin Chen, Shuai Zhang, Boran Han +1
In-context learning (ICL) involves reasoning from given contextual examples. As more modalities comes, this procedure is becoming more challenging as the interleaved input modaliti…