2 citations · 3 across the 8 of their papers we have counts for
8 papers
Exploring Spatial Intelligence from a Generative Perspective
Muzhi Zhu, Shunyao Jiang, Huanyi Zheng +9
Spatial intelligence is essential for multimodal large language models, yet current benchmarks largely assess it only from an understanding perspective. We ask whether modern gener…
Unified Open-World Segmentation with Multi-Modal Prompts
Yang Liu, Yufei Yin, Chenchen Jing +7
In this work, we present COSINE, a unified open-world segmentation model that consolidates open-vocabulary segmentation and in-context segmentation with multi-modal prompts (e.g.,…
SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories
Muzhi Zhu, Yuzhuo Tian, Hao Chen +5
While MLLMs have demonstrated adequate image understanding capabilities, they still struggle with pixel-level comprehension, limiting their practical applications. Current evaluati…
Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation
Muzhi Zhu, Yang Liu, Zekai Luo +5
The Diffusion Model has not only garnered noteworthy achievements in the realm of image generation but has also demonstrated its potential as an effective pretraining method utiliz…
A Simple Image Segmentation Framework via In-Context Examples
Yang Liu, Chenchen Jing, Hengtao Li +4
Recently, there have been explorations of generalist segmentation models that can effectively tackle a variety of image segmentation tasks within a unified in-context learning fram…
Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond
Minghao Liu, Zonglin Di, Jiaheng Wei +15
Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning specialized models. However, creating…
Generative Active Learning for Long-tailed Instance Segmentation
Muzhi Zhu, Chengxiang Fan, Hao Chen +4
Recently, large-scale language-image generative models have gained widespread attention and many works have utilized generated data from these models to further enhance the perform…
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative Data
Chengxiang Fan, Muzhi Zhu, Hao Chen +4
Instance segmentation is data-hungry, and as model capacity increases, data scale becomes crucial for improving the accuracy. Most instance segmentation datasets today require cost…