activity
20222026
most citedQwen3-VL Technical Report

16 citations · 29 across the 26 of their papers we have counts for

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Showing cs.CLShow all

7 papers · 1 filter

cs.CL2025★ 1 cited

Omni-Captioner: Data Pipeline, Models, and Benchmark for Omni Detailed Perception

Ziyang Ma, Ruiyang Xu, Zhenghao Xing +9

Fine-grained perception of multimodal information is critical for advancing human-AI interaction. With recent progress in audio-visual technologies, Omni Language Models (OLMs), ca…

cs.CL2025★ 2 cited

Qwen3-Omni Technical Report

Jin Xu, Zhifang Guo, Hangrui Hu +35

We present Qwen3-Omni, a single multimodal model that, for the first time, maintains state-of-the-art performance across text, image, audio, and video without any degradation relat…

cs.CL2025

TokenSwift: Lossless Acceleration of Ultra Long Sequence Generation

Tong Wu, Junzhe Shen, Zixia Jia +2

Generating ultra-long sequences with large language models (LLMs) has become increasingly crucial but remains a highly time-intensive task, particularly for sequences up to 100K to…

cs.CL2024

Friends-MMC: A Dataset for Multi-modal Multi-party Conversation Understanding

Yueqian Wang, Xiaojun Meng, Yuxuan Wang +3

Multi-modal multi-party conversation (MMC) is a less studied yet important topic of research due to that it well fits real-world scenarios and thus potentially has more widely-used…

cs.CL2023

Teaching Text-to-Image Models to Communicate in Dialog

Xiaowen Sun, Jiazhan Feng, Yuxuan Wang +3

A picture is worth a thousand words, thus, it is crucial for conversational agents to understand, perceive, and effectively respond with pictures. However, we find that directly em…

cs.CL2023

Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language Pre-training

Yuxuan Wang, Jianghui Wang, Dongyan Zhao +1

We introduce CDBERT, a new learning paradigm that enhances the semantics understanding ability of the Chinese PLMs with dictionary knowledge and structure of Chinese characters. We…

cs.CL2022★ 1 cited

Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation

Xueliang Zhao, Yuxuan Wang, Chongyang Tao +2

We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video. The primary challenges of this task lie in (1) th…