32 citations · 58 across the 13 of their papers we have counts for
14 papers
StepAudio 3 Realtime Technical Report
Bin Lin, Bo Zhao, Boyang Zhang +87
Realtime spoken interaction demands deep reasoning, prompt responses, and fluid turn-taking. We present StepAudio 3 Realtime, an audio-language foundation model organized around a…
PCA: Persistence-Aware Compression and Aggregation for Fast Video Large Language Models
Zihan Song, Shuo Ye, Bo Zhao +4
Despite advances in Video Large Language Models (VLLMs) that have displayed promising outcomes in video understanding, the redundancy in the long-duration frames remains a hindranc…
Homer: Understanding Long-form Videos with Hierarchical Memory and Agentic Reasoning
Yixin Ji, Fanghua Ye, Juntao Li +5
Multimodal large language models excel on short clips but struggle on hour-long videos in an online setting, where frames are processed incrementally under limited memory. Existing…
StepAudio 2.5 Technical Report
Bin Lin, Bo Zhao, Boyong Wu +98
Unified audio-language modeling has emerged as a prominent trend in modern speech systems, promising to bring the reasoning capabilities of large language models to auditory tasks.…
AffectVerse: Emotional World Models for Multimodal Affective Computing
Bo Zhao, Fanghua Ye, Yixin Ji +3
Humans infer emotions by integrating observed multimodal cues with expectations about how affective states may unfold. Existing multimodal large language models (MLLMs), however, o…
UniPPTBench: A Unified Benchmark for Presentation Generation Across Diverse Input Settings
Bo Zhao, Maosheng Pang, Chen Zhang +3
Existing works typically focus on presentation generation under isolated input settings, whereas real-world use cases span diverse scenarios, including vague user prompts, long doc…
Navigating the Emotion Tree: Hierarchical Hyperbolic RAG for Multimodal Emotion Recognition
Zeheng Wang, Bo Zhao, Yijie Zhu +6
Multimodal emotion recognition aims to integrate text, audio, and video sources to understand human affective states. Although multimodal large language models excel at multimodal…
Firebolt-VL: Efficient Vision-Language Understanding with Cross-Modality Modulation
Quoc-Huy Trinh, Mustapha Abdullahi, Bo Zhao +1
Recent advances in multimodal large language models (MLLMs) have enabled impressive progress in vision-language understanding, yet their high computational cost limits deployment i…
Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly
Yexin Liu, Zhengyang Liang, Yueze Wang +8
Multimodal Large Language Models (MLLMs) have displayed remarkable performance in multi-modal tasks, particularly in visual comprehension. However, we reveal that MLLMs often gener…
Efficient Multimodal Large Language Models: A Survey
Yizhang Jin, Jian Li, Yexin Liu +10
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning.…
FlexiFilm: Long Video Generation with Flexible Conditions
Yichen Ouyang, jianhao Yuan, Hao Zhao +2
Generating long and consistent videos has emerged as a significant yet challenging problem. While most existing diffusion-based video generation models, derived from image generati…
SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Model
Bin Cao, Jianhao Yuan, Yexin Liu +4
In the rapidly evolving area of image synthesis, a serious challenge is the presence of complex artifacts that compromise perceptual realism of synthetic images. To alleviate artif…
Efficient Multimodal Learning from Data-centric Perspective
Muyang He, Yexin Liu, Boya Wu +4
Multimodal Large Language Models (MLLMs) have demonstrated notable capabilities in general visual understanding and reasoning tasks. However, their deployment is hindered by substa…
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model
Jianhao Yuan, Shuyang Sun, Daniel Omeiza +4
We need to trust robots that use often opaque AI methods. They need to explain themselves to us, and we need to trust their explanation. In this regard, explainability plays a crit…