3 papers
cs.CV2026
EgoPro-Bench: Benchmarking Personalized Proactive Interaction in Egocentric Video Streams
Dongchuan Ran, Linyu Ou, Xueheng Li +5
Existing Multimodal Large Language Models (MLLMs) remain primarily reactive, failing to continuously perceive environments or proactively assist users. While emerging benchmarks ad…
cs.CV2025
InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn Dialogue
Wenwen Tong, Hewei Guo, Dongchuan Ran +23
We introduce InteractiveOmni, a unified and open-source omni-modal large language model for audio-visual multi-turn interaction, ranging from 4B to 8B parameters, designed to lead…
cs.CV2025
Test-Time Temporal Sampling for Efficient MLLM Video Understanding
Kaibin Wang, Mingbao Lin
Processing long videos with multimodal large language models (MLLMs) poses a significant computational challenge, as the model's self-attention mechanism scales quadratically with…