2 citations · 2 across the 5 of their papers we have counts for
5 papers
Visual Reasoning through Tool-supervised Reinforcement Learning
Qihua Dong, Gozde Sahin, Pei Wang +4
In this paper, we investigate the problem of how to effectively master tool-use to solve complex visual reasoning tasks for Multimodal Large Language Models. To achieve that, we pr…
Learning Compact Video Representations for Efficient Long-form Video Understanding in Large Multimodal Models
Yuxiao Chen, Jue Wang, Zhikang Zhang +8
With recent advancements in video backbone architectures, combined with the remarkable achievements of large language models (LLMs), the analysis of long-form videos spanning tens…
Open-World Dynamic Prompt and Continual Visual Representation Learning
Youngeun Kim, Jun Fang, Qin Zhang +7
The open world is inherently dynamic, characterized by ever-evolving concepts and distributions. Continual learning (CL) in this dynamic open-world environment presents a significa…
NAVERO: Unlocking Fine-Grained Semantics for Video-Language Compositionality
Chaofan Tao, Gukyeong Kwon, Varad Gunjal +7
We study the capability of Video-Language (VidL) models in understanding compositions between objects, attributes, actions and their relations. Composition understanding becomes pa…
Mixed-Query Transformer: A Unified Image Segmentation Architecture
Pei Wang, Zhaowei Cai, Hao Yang +3
Existing unified image segmentation models either employ a unified architecture across multiple tasks but use separate weights tailored to each dataset, or apply a single set of we…