1 citations · 2 across the 13 of their papers we have counts for
8 papers · 1 filter
MASA: Rethinking the Representational Bottleneck in LoRA with Multi-A Shared Adaptation
Qin Dong, Yuntian Tang, Heming Jia +7
Low-Rank Adaptation (LoRA) has emerged as a dominant method in Parameter-Efficient Fine-Tuning (PEFT) for large language models, which augments the transformer layer with one down-…
MT: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning
Zhaopeng Feng, Yupu Liang, Shaosheng Cao +7
Text Image Machine Translation (TIMT)-the task of translating textual content embedded in images-is critical for applications in accessibility, cross-lingual information access, an…
CompBench: Benchmarking Complex Instruction-guided Image Editing
Bohan Jia, Wenxuan Huang, Yuntian Tang +14
While real-world applications increasingly demand intricate scene manipulation, existing instruction-guided image editing benchmarks often oversimplify task complexity and lack com…
ReactDance: Hierarchical Representation for High-Fidelity and Coherent Long-Form Reactive Dance Generation
Jingzhong Lin, Xinru Li, Yuanyuan Qi +8
Reactive dance generation (RDG), the task of generating a dance conditioned on a lead dancer's motion, holds significant promise for enhancing human-robot interaction and immersive…
TimeSoccer: An End-to-End Multimodal Large Language Model for Soccer Commentary Generation
Ling You, Wenxuan Huang, Xinni Xie +5
Soccer is a globally popular sporting event, typically characterized by long matches and distinctive highlight moments. Recent advances in Multimodal Large Language Models (MLLMs)…
VCR-Bench: A Comprehensive Evaluation Framework for Video Chain-of-Thought Reasoning
Yukun Qi, Yiming Zhao, Yu Zeng +7
The advancement of Chain-of-Thought (CoT) reasoning has significantly enhanced the capabilities of large language models (LLMs) and large vision-language models (LVLMs). However, a…
LLaVA-RadZ: Can Multimodal Large Language Models Effectively Tackle Zero-shot Radiology Recognition?
Bangyan Li, Wenxuan Huang, Zhenkun Gao +8
Recently, Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in visual understanding and reasoning across various vision-language tasks. However, w…
Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
Wenxuan Huang, Bohan Jia, Zijie Zhai +7
DeepSeek-R1-Zero has successfully demonstrated the emergence of reasoning capabilities in LLMs purely through Reinforcement Learning (RL). Inspired by this breakthrough, we explore…