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Yang Miao

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3 papers

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NeurIPS Conference 2025 Conference Paper

LangHOPS: Language Grounded Hierarchical Open-Vocabulary Part Segmentation

Yang Miao
Jan-Nico Zaech
Xi Wang
Fabien Despinoy
Danda Pani Paudel
Luc V Gool

We propose LangHOPS, the first Multimodal Large Language Model (MLLM)-based framework for open-vocabulary object–part instance segmentation. Given an image, LangHOPS can jointly detect and segment hierarchical object and part instances from open-vocabulary candidate categories. Unlike prior approaches that rely on heuristic or learnable visual grouping, our approach grounds object–part hierarchies in language space. It integrates the MLLM into the object-part parsing pipeline to leverage rich knowledge and reasoning capabilities, and link multi-granularity concepts within the hierarchies. We evaluate LangHOPS across multiple challenging scenarios, including in-domain and cross-dataset object-part instance segmentation, and zero-shot semantic segmentation. LangHOPS achieves state-of-the-art results, surpassing previous methods by 5. 5% Average Precision(AP) (in-domain) and 4. 8% (cross-dataset) on the PartImageNet dataset and by 2. 5% mIOU on unseen object parts in ADE20K (zero-shot). Ablation studies further validate the effectiveness of the language-grounded hierarchy and MLLM-driven part query refinement strategy.

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IROS Conference 2024 Conference Paper

Volumetric Semantically Consistent 3D Panoptic Mapping

Yang Miao
Iro Armeni
Marc Pollefeys
Daniel Barath

We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating comprehensive, accurate, and efficient semantic 3D maps suitable for autonomous agents in unstructured environments. The proposed approach is based on a Voxel-TSDF representation used in recent algorithms. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. Further improvements are achieved by graph optimization-based semantic labeling and instance refinement. The proposed method achieves accuracy superior to the state of the art on public large-scale datasets, improving on a number of widely used metrics. We also highlight a downfall in the evaluation of recent studies: using the ground truth trajectory as input instead of a SLAM-estimated one substantially affects the accuracy, creating a large gap between the reported results and the actual performance on real-world data. The code is available: https://github.com/y9miao/ConsistentPanopticSLAM.

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JBHI Journal 2023 Journal Article

CCS-Net: Cascade Detection Network With the Convolution Kernel Switch Block and Statistics Optimal Anchors Block in Hypopharyngeal Cancer MRI

Shuo Zhang
Yang Miao
Jun Chen
Xiwei Zhang
Lei Han
Zehao Huang
Ning Pei
Haibin Liu

Magnetic resonance imaging (MRI) is a common diagnostic method for hypopharyngeal cancer (HPC). It is a challenge to automatically detect HPC tumors and swollen lymph nodes (HPC risk areas) from MRI slices because of the small size and irregular shape of HPC risk areas. Herein, we propose a cascade detection network with Convolution Kernel Switch (CKS) Block and Statistics Optimal Anchors (SOA) Block in HPC MRI (CCS-Net). CKS Block can adaptively switch standard convolution to deformable convolution in some appropriate layers to detect irregular objects more efficiently without taking up too much computing resources. SOA Block can automatically generate the optimal anchors based on the size distribution of objects. Compared with other methods, our method achieves splendid detection performance and outperforms other methods on the HPC dataset (more than 1800 T2 MRI slices), achieving the highest AP 50 of 78. 90%. Experiments show that the proposed network can be the basis of a computer aided diagnosis utility that helps achieve faster and more accurate diagnostic decisions for HPC.

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