8 citations · 10 across the 9 of their papers we have counts for
8 papers · 1 filter
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…
AndroidLens: Long-latency Evaluation with Nested Sub-targets for Android GUI Agents
Yue Cao, Yingyao Wang, Pi Bu +10
Graphical user interface (GUI) agents can substantially improve productivity by automating frequently executed long-latency tasks on mobile devices. However, existing evaluation be…
EgoExoBench: A Benchmark for First- and Third-person View Video Understanding in MLLMs
Yuping He, Yifei Huang, Guo Chen +4
Transferring and integrating knowledge across first-person (egocentric) and third-person (exocentric) viewpoints is intrinsic to human intelligence, enabling humans to learn from o…
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…
Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision
Yuping He, Yifei Huang, Guo Chen +5
Perceiving the world from both egocentric (first-person) and exocentric (third-person) perspectives is fundamental to human cognition, enabling rich and complementary understanding…
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…
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Jinguo Zhu, Weiyun Wang, Zhe Chen +48
We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model…
Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models
Zhiqi Li, Guo Chen, Shilong Liu +24
Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models. However, most…