LangHOPS: Language Grounded Hierarchical Open-Vocabulary Part Segmentation

• URL: http://arxiv.org/abs/2510.25263v2
• Date: Fri, 31 Oct 2025 09:11:14 GMT
• Title: LangHOPS: Language Grounded Hierarchical Open-Vocabulary Part Segmentation
• Authors: Yang Miao, Jan-Nico Zaech, Xi Wang, Fabien Despinoy, Danda Pani Paudel, Luc Van Gool,
• Abstract summary: We propose LangHOPS, the first Multimodal Large Language Model (MLLM) based framework for open-vocabulary object-part instance segmentation.<n>LangHOPS can jointly detect and segment hierarchical object and part instances from open-vocabulary candidate categories.
• Score: 56.12844551763724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 its 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. The code will be released here.

Related papers

• Bootstrapping MLLM for Weakly-Supervised Class-Agnostic Object Counting [59.37613121962146]
  We propose WS-COC, the first MLLM-driven weakly-supervised framework for class-agnostic object counting.<n> WS-COC matches or even surpasses many state-of-art fully-supervised methods while significantly reducing annotation costs.
  arXiv  Detail & Related papers  (2026-02-13T09:58:35Z)
• RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation [78.01030342481246]
  RecBase is a domain-agnostic foundational model pretrained with a recommendation-oriented objective.<n>We introduce a unified item tokenizer that encodes items into hierarchical concept identifiers.<n>Our model matches or surpasses the performance of LLM baselines up to 7B parameters in zero-shot and cross-domain recommendation tasks.
  arXiv  Detail & Related papers  (2025-09-03T08:33:43Z)
• Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation [0.0]
  LangSeg is a novel semantic segmentation method that leverages context-sensitive, fine-grained subclass descriptors.<n>We evaluate LangSeg on two challenging datasets, ADE20K and COCO-Stuff, where it outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2025-01-27T20:02:12Z)
• CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models [2.331828779757202]
  We present CALICO, the first Large Vision-Language Models (LVLM) designed for multi-image part-level reasoning segmentation.<n> CALICO features two key components, a novel Correspondence Extraction Module that identifies semantic part-level correspondences, and Adaptation Correspondence Modules that embed this information into the LVLM.<n>We show that CALICO, with just 0.3% of its parameters finetuned, achieves strong performance on this challenging task.
  arXiv  Detail & Related papers  (2024-12-26T18:59:37Z)
• Lidar Panoptic Segmentation in an Open World [50.094491113541046]
  Lidar Panoptics (LPS) is crucial for safe deployment of autonomous vehicles. LPS aims to recognize and segment lidar points wr.t. a pre-defined vocabulary of semantic classes. We propose a class-agnostic point clustering and over-segment the input cloud in a hierarchical fashion, followed by binary point segment classification.
  arXiv  Detail & Related papers  (2024-09-22T00:10:20Z)
• PartGLEE: A Foundation Model for Recognizing and Parsing Any Objects [104.34288029037141]
  We present PartGLEE, a part-level foundation model for locating and identifying both objects and parts in images. PartGLEE accomplishes detection, segmentation, and grounding of instances at any granularity in the open world scenario.
  arXiv  Detail & Related papers  (2024-07-23T17:58:26Z)
• SPIN: Hierarchical Segmentation with Subpart Granularity in Natural Images [17.98848062686217]
  We introduce the first hierarchical semantic segmentation dataset with subpart annotations for natural images. We also introduce two novel evaluation metrics to evaluate how well algorithms capture spatial and semantic relationships across hierarchical levels.
  arXiv  Detail & Related papers  (2024-07-12T21:08:00Z)
• Hierarchical Open-vocabulary Universal Image Segmentation [48.008887320870244]
  Open-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions. We propose a decoupled text-image fusion mechanism and representation learning modules for both "things" and "stuff" Our resulting model, named HIPIE tackles, HIerarchical, oPen-vocabulary, and unIvErsal segmentation tasks within a unified framework.
  arXiv  Detail & Related papers  (2023-07-03T06:02:15Z)
• Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation [80.48979302400868]
  We focus on open vocabulary instance segmentation to expand a segmentation model to classify and segment instance-level novel categories. Previous approaches have relied on massive caption datasets and complex pipelines to establish one-to-one mappings between image regions and captions in nouns. We devise a joint textbfCaption Grounding and Generation (CGG) framework, which incorporates a novel grounding loss that only focuses on matching object to improve learning efficiency.
  arXiv  Detail & Related papers  (2023-01-02T18:52:12Z)
• TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation [44.75300205362518]
  Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. We propose the first top-down unsupervised semantic segmentation framework for fine-grained segmentation in extremely complicated scenarios. Our results show that our top-down unsupervised segmentation is robust to both object-centric and scene-centric datasets.
  arXiv  Detail & Related papers  (2021-12-02T18:59:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.