5 papers
Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline
Guo Chen, Lidong Lu, Yicheng Liu +17
While datasets for video understanding have scaled to hour-long durations, they typically consist of densely concatenated clips that differ from natural, unscripted daily life. To…
Learning Visual Affordance from Audio
Lidong Lu, Guo Chen, Zhu Wei +2
We introduce Audio-Visual Affordance Grounding (AV-AG), a new task that segments object interaction regions from action sounds. Unlike existing approaches that rely on textual inst…
AV-Reasoner: Improving and Benchmarking Clue-Grounded Audio-Visual Counting for MLLMs
Lidong Lu, Guo Chen, Zhiqi Li +2
Despite progress in video understanding, current MLLMs struggle with counting tasks. Existing benchmarks are limited by short videos, close-set queries, lack of clue annotations, a…
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
Guo Chen, Zhiqi Li, Shihao Wang +16
We introduce Eagle 2.5, a family of frontier vision-language models (VLMs) for long-context multimodal learning. Our work addresses the challenges in long video comprehension and h…
CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding
Guo Chen, Yicheng Liu, Yifei Huang +6
Most existing video understanding benchmarks for multimodal large language models (MLLMs) focus only on short videos. The limited number of benchmarks for long video understanding…