LangHOPS: Language Grounded Hierarchical Open-Vocabulary Part Segmentation
Authors: Yang Miao
Jan-Nico Zaech
Xi Wang
Fabien Despinoy
Danda Pani Paudel
Luc Van Gool
Venue: NeurIPS 2025 poster ()
Keywords: Object Part Segmentation; Language Grounding; Multi-modality; Multimodal Large Language Model
Primary area: applications
TL;DR: We propose LangHOPS, the first Multimodal Large Language Model (MLLM)-based framework for open-vocabulary object–part instance segmentation.

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 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.

LangHOPS: Language Grounded Hierarchical
Part Segmentation
Yang Miao INSAIT, Sofia University "St. Kliment Ohridski"
Jan-Nico Zaech INSAIT, Sofia University "St. Kliment Ohridski"
Xi Wang INSAIT, Sofia University "St. Kliment Ohridski" ETH Zurich, TU Munich
Fabien Despinoy Toyota Motor Europe
Danda Pani Paudel INSAIT, Sofia University "St. Kliment Ohridski"
Luc Van Gool
INSAIT, Sofia University "St. Kliment Ohridski"
Oiject Segmemmttio
Qutr
Image
Features
Mentaaccy in Langagge Space
Ui-oose Parking
OUIPF
Part Segmentatii
LangHOPS
Predictions of LangHOPS
Figure 1: Given 2D image and user queries of candidate object-part categories, our method LangHOPS grounds the hierarchy between objects and parts in language space and subsequently leverages a Multimodal Large Language Model to break down the segmented objects into parts.
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-vocahulary candidate categories. Unlike prior approaches that rely on heuristic or learnable visual grouping, our approach grounds object-part hierarchies language space. It integrates the MLLM into the object-part pars- ing pipeline to leverage its rich knowledge and reasoning capabilities, and link multi multi-granullrity concepts within the hierarchies. We evaluate LangHOPS across multiple challenging scenarios, including in in-domiin and cross-dataset object-pant part instance segmentation, and zero-shot semantic segmentation. LangHOPS achieves state-of-the-arr afrt results, surpassing previous methods by 5.5% Average Precision (AP) (in-domain) and 4.8% (cross-datasel) on the PartImageNet dataset and by 2.5% mTOU unseen object parts in ADE20K (zero-shot). Ablation studies further validate the effectiveness of the language- grounded hierarchy and MILM-- driven part query refinement strategy. The code will be released here
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
2D instance segmentation is well-established computer vision research field and has experienced
significant progress in object-level instance segmentation in the past decades 66
17
54
64
69
While recent efforts have expanded toward higher-level reasoning from visual input 22
54
the growing demand for finer semantic understanding has led to increased interest in pert- segmentation [37] 50, 58). Unlike object-level segmentation, part-level understanding introduces new challenges, as it requires richer contextual awareness, reasoning about object-pes -pat relationships, and task-dependent interpretation. For example, a car can break down into coarse- grainnegggeed components such the body and wheels, further delineated into finer-grained elements, including windows, doors, headlights, mirrors, or screws, depending on the downstream task and desired granularity.
nren-Vocabulary object-Part Instance Segmentation (OVPIS) emerges as promising approach to address this challenge and has gained increasing interest in recent years. Unlike open-vocabulary object-part semantic segmentation 8 9 555) which assigns part labels to pixels without distinguishing between multiple part instances, object-part instance segmentation requires detecting and segmenting object and part instances separately. This introduces additional complexity, as the model must establish part-whole relationships at the instance level and maintain consistent grouping between objects and their constituent parts. In contrast to dlosed-occabulary settings that rely on predefined object-par part lists, apen-vocabylary models aim to generalize to unseen part categories and novel compositions, which is key capability for real-world generalization. Among existing works, SAM 20 relies on handcrafted object-part and subpart heuristics for part-level segmentation. However, does not offer control over the semantic granularity of the parts. Recent works [46] 63 extend SAM with a text prompt module to guide the segmentation process, but lack modeling of object-part hierarchies, which limits their ability to reason about relationships between objects and corresponding parts.
Moving beyond interactive prompt-tuned variants of SAM, separate line of work focuses on open-vocabulary part segmentation by leveraging vision-language models. OV-Parts (55), Part- CLIPSeg 1.. and ParCAATSeg [8] implement object-part hierarchical reasoning implicitly in CLIP embedding space [41 and enable zero-shot transfer to novel part categories. However, the perfor- mance of these methods is constrained by the limitations of CLIP in compositional and pert-level understanding [ [53] PartGLEE [4]] explicitly models object-part structures using a Q-Former and performs joint object and part instance segmentation. Nevertheless, it has suboptimal segmen- tation performance in open-vocabulary scenarios since the Q-Former module lacks mechanisms to handle part granularity variations. Addressing this limitation is essential for improving generalization in real-world applications, where part granularity naturally varies across contexts and user intentions. For example, operating laptop may require segmenting coarse parts such as the lid, while repairing it demands finer segmentation of detailed components such as screws or hinges.
In contrast, propose LangHOPS, novel framework that leverages language-grouu grounded hierarchy and integrates MLLM for the task of OVPIS, as shown in Fig. [ hierarchies directly in the language space, producing language-grounded
LangHOPS embeds object-put grounded part queries with object context. Those queries are further processed by a MI.LMM to link compositional object-part concepts and to generate adaptive segmentation queries. To verify the performance of LangHOPS, we conduct experiments in multiple settings (in-domain, cross-dataset and zero-shot) and on multiple dataset (Par- UmageNet, PascalPart- 1ilP 116 and ADE20K). As result, LongHOPS significantly outperforms baselines by 5.5% AP (in-domain) and 4.8% AP (cross-dataset) on the PartImageNet dataset and by 2.5 % m/OU unseen on ADE20K (zero-shot). Experiment also shows the advanced scalability of LangHOPS with improvement by 10.0% AP on PartImageNet when trained on more dataset, Ablation study shows that LangHOPS have object-part synergy that pert-level instance segmentation can improve object segmentation by 5.4% AP. In summary, our key contributions are:
We propose LangHOPS, the first framework integrating MILM for the task of OVPIS. We propose language-space grounded object-part hierarchy modeling for part query representation and link the guanuiigranllarity concepts with MILLM to enable context-aware and accurate object-part parsing.
We conduct experiments and demonstrate superior performance of LangHOPS in in-domain, cross- dataset, and zero-shot settings, as well as its scalability when on larger datasets. Notably, we show for the first time that level supervision can significantly enhance object-level segmentation.

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2
Related Work
2D Object-Parte Segmentation aims to jointly detect and segment both objects and their semantic
parts, while preserving the hierarchical structure between them 10
16
67 This task goes beyond
traditional object-level understanding 2 13 26
57
65
68 by introducing part-level pennt-llaggranlarriii
within object instances. This topie has gained attention 8
24
25
35
39
533
due its potential
29 and robotics [5] 36., TAPPS [10] extends Mask2Former [6] to predict jointly objects and parts with set of shared queries. However, it is limited to a fixed set of predefined categories. PartCLIPSeg [9] applies a two-stage strategy for part-level semantic segmentation by first extracting mask proposals and then applying CLIP 4 to classify the masked image crops. Nevertheless, CLIP-based approaches such as PartCLIPSeg [9] and OV-Part [55] often exhibit suboptimal performance in fine- ane-grained part segmentation, largely due to CLIP's s limited capacity for compositional reasoning and explicit modeling of object-part
in downstream applications such as image editing 19
hierarchies I 51 55 More recently, ParCATTSeg [ introduces a cost aggregation framework with a compositional loss and DINO-based structural guidance to enhance part-level image text alignment and structural understanding. However, this method still lacks an explicit, language- grounded mechanism for representing hierarchical object-part relationships, which is essential for robust compositional generalization. Separately, PanGLEE (24) adopts a different two-stage pipeline that first segments object instances and then parses object queries into parts using Q-Former. Since the Q-Former in PartGLEE [24] is not explicitly aware of part granularity during training or inference, it struggles to adapt across datasets with differrig levels of annotation detail. For instance, a model trained on fine-gran -re-graindd parts such as "eye," " "nose," and "ear" for cats in Pascal-Part- performs poorly PartImageNet, where the same category is annotated only with coarser parts ("head," "body," "foot," and "tail"). Although g (4 incorporate object-level context into part segmentation, they do not entirely leverage the hierarchical relationships between objects and parts from candidate category definitions, consequently limiting their overall performance. In contrast, LOngHOPS explicitly embeds the object-part hierarchies in language space guide the MLLM for object-part parsing, as detailed in Sec. 3.4
Open- Vocabulary Segmentation requires models to detect and segment object parts from novel categories guided by free-form text descriptions, without relying on category-specific training data. Early works, such as MaskCLIP [12] and Gro (57). initiated this paradigm by using Vision Language Models (VLMs) to transfer knowledge from text supervision to pixel-level tasks. Follow- up methods (26) 66 further enhance this capability by introducing text embeddings into mask prediction, contrastive learning, or region-level alignment. These approaches demonstrate the flexible and scalable supervision signal for segmentation tasks.
potential of using language
However, most of the existing works [12 26 30 38
66) focus on object-level semantics, consequently lacking fine-grained part-level reasoning. OV-Part 55 and VLPart (50) establish benchmarks for open-vocabulary part segmentation by augmenting existing datasets with part-level annotations 4 [6 42 67). Although recent methods [8 24 50 55) make progress towards an open- vocabulary setting. they still exhibit limited generalization, particularly in zero-shot and cross-dataset scenarios where both the label space and data distribution differ from those seen during training. LangHOPS leverages MLLMs and object-pas -part hicrarchies to improve generalization and accuracy in the OVPIS task, setting new benchmark in the open- open-vocabulary zero-shot and cross-dataset settings.
44
45
[57
MLLM-based Image Segmentation integrates multimodal language models into image segmen- tation tasks, unlocking strong performance in various domains such as open-vocabulary panoptic segmentation, referring segmentation, interactive segmentation, and reasoning-base
segmenta- LISA 22 introduces "reasoning segmentation". allowing MLLMs generate the mask token in response to complex and implicit textual queries. PSALM (64) extends L.LMs with a vision encoder and mask decoder with a flexible input prompt to handle diverse segmentation tasks. OMG-LLaVA [62] proposes an end-to-ent MLLM-based framework capable of image-, object- and pixel-level understanding including pixel-Ievel segmentation. While effective, these methods focus on object-level understanding and lack the ability to decompose objects into fine-grained semantic parts. Osprey (58) achieves part-level visual understanding but relies on off-the-shell class- aansssii part masks (e.g. from SAM (20]) and cannot control over part granularity. More recently, CALICO [37] leverages MLLM for multi-image part-focused object comparison by identifying unique and common parts of certain object across images. In contrast, HOnS is the first framework to leverage object-part instance segmentation, enabling fine- grained parsing at the instance level, beyond the semantic and multi-image settings explored in prior work.
tion 22
MILMMs for
3

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Segmenntatin 3.3)
Langunge Grounded
Object Decoder
Mg
Obar Colrgetes
bust
cari (03
Visse Baxkbone
Powd Decoder
Image Backbone
Object-Part Parsing
SiLiP
Pranpe Pluase parssaanpariinng
y Image
Inpurt Image
Germin
Lono
Deyyeer....
MLLM--aased
Pursing
Part Decoder
Mariaa
My
LangHOPS
Segmentation (Sec 3.5)
Object:Part Parsing
LangHOPS framework. The left block illustrates the overall architecture, with an image backbone, object segmentation module, object-part parser and part segmentation module. The right block illustrates the ideas on the object-part parser, consisting of a "Language-Grouu Grounded Hicrarchies" module embedding the object-part hicrarchy language space, and "MLLM-base Parsing" module producing the part queries for segmentation using a MLLM.
Figure 2:
3 Method
3.1 Problem Definition
OVPIS aims to segment an image into distinct object-level instances and object-specific part-level instances, with the capability to generalize in novel object-part categories. Given an image and user- defined open-vocabulary object-part categories, the model outputs masks and categories of objects with their corresponding parts (e.g., "bus 1", "bus I's headlight 1", "bus I's headlight 2", etc.). Note that, in contrast to the semantic part segmentation task proposed in (55], OVPIS also distinguishes between different instances of the same object category. For open- goe--occayllary segmentation, we adopt the commonly used settings in prior work [50] where the model takes images and ground truth mask pairs, for one set of object and part categories Cumin during training, and segment objects and parts of novel categories Cnovel during inference.
3.2 Method Overview
Our model is illustrated in Fig. Given an input RGB image I with a set of user-defined open- vocabulary candidate objects and part categories C, the model outputs the masks and categories of the segmented instances of objects and parts, as well as the object-part hierarchies between the instances. Specifically, our framework in Fig. P composed as follow:
Object Segmentation. We derive the initial object queries Oo with prior information from image features X, and apply the object decoder utilized in (24) together with CLIP text encoder to obtain predictions of object-level categories Co. bounding boxes Bo and segmentation masks Mo.
Language-grounder Hierarchies. We first extract hierarchies between objects and parts from the input candidate categories C and encode them CLIP's language space E(C), Subsequently, given predictions of objects' categories, we construct initial part-level queries po by retrieving object- conditioned part embeddings from E(C), and concatenating them with object queries, enabling context-aware and aranularity-ddapiiee object-part parsing.
MLLM-based Parsing, We leverage a MLLM to refine initial by linking visual concepts with producing enriched part queries P that capture hierarchical part relationships across both language and visual domains for subsequent decoding.
conttcoonatee queries po concatentede queries through structured prompt guidance,
Part segmentation. We use P and X inputs to the part decoder with the same structure as the object decoder and predict categories Cp, bounding boxes Bp and masks Mp of part instances.

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By integrating the our object and part segmentation modules are tightly coupled. Joint information between parts and objects is utilized through the following information flow: For each segmented object, set of part queries is constructed by concatenation of the object embedding generated in the object decoder and the aanguage-encoder part description. These queries are processed by the MILM, generating queries capable of -ocaboocyyy part segmentation. The processed queries are used in the part decoder to segment each requested part. The details are provided in the following subsections.
hierarchies and MLLM into the object- -art parsing framework,
3.3 Object Segmentation
Following PanGLEE [24], we apply the transformer decoder implemented in 23 object decoder. Object queries O E R DNDD, are initialized given priors from the multi-scale image features
X, E RD.x # s {2, 3, 4, 5), with N as hyper-paramete denoting the number of object queries. Next, L layers of deformable transformer decoder module [23] are applied for cross- attention computation between X, and O', as well as self-attention of O', i E [1, The output queries 0 are utilized to perform object-level detection, classification and segmentation with separate prediction head. For detection, 3-layer MLP is utilized to map O to the coordinates of the bounding boxes B RNX,
B MLP(OL))
(I)
For open-vocabulary object-level classification, we apply the CLIP text encoder to process the user-defined candidate object categories and obtain the object-level class embeddings:
E E(Cnn)) = CLoPttx (Cobj)
Then the classification logits are calculated by:
s = (00) -E(Cobj):
where fco is the linear layer mapping O4 to CLIP embedding space. By taking the maximum logits over the candidate categories, semantic categories predictions, CO are obtained. For object segmentation, masks are generated by calculating the inner-product between 04 and the dense mask features fas (X2) obtained with 2D convolutional network fM the dense features X2:
M [ (X2),
where faso is 3-layer MLP mapping queries into mask features's 's embedding space.
3.4 Language-grounded Hierarchies
Following object segmentation, the next objective is to decompose each segmented object into its corresponding part-level instances. This requires first modeling the relationship between objects and their constituent parts. PartGLEB 24 addresses this by introducing set of learnable, universal parsing queries that, together with object queries, are processed by a Q-Former to generate a fixed number of part queries for each object. However, such D-Former-based object-part parsing method has inherent limitations. First, it lacks of context awareness as it does not incorporate user-defined open- vocabulary categories C during object-part parsing. Consequently, the model may fail to generalize across domains where the definition of parts differs (e.g., coarse vs. fine-grained part sets). Second, the Q-Former-basee method suffers from limited generalization from data priors. In fact, it has to be entirely trained and lacks external knowledge, which makes it highly dependent on the distribution and coverage of the training data.
To effectively address these issues, we explicitly model the hierarchical object-part structure from C in the well-geneeallzzable language space. Specifically, given one object 04 e RLxD, and its predicted object category C, from Sec. 3.3 we query its potential part categories using C: Cpart- For example, if an object with the query O L is classified as "bus", then we retrieve all the parts belonging to "bus" from C ("bus's wheel", "bus's window", "bus's door". etc). Subsequently, encode the retrieved part categories into the CLIP text embedding space:
E(Cpart (C.)) (CIIP Peet (CE)),CC C (C.)
where (C,) represents part categories belonging to corresponding object category C., Ulti- mately, the embedding of each candidate part concatenated with the corresponding object query separately as the initial part queries, with both object-level context and part-level language priors:
pi (0" fco(e)) e E(Cpart (C.)),
(6)
5

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where (-Il-) represents the concatenation of two tensors. The query Pf is beyond pure text embeddings. Instead, it incorporates both visual information and language semantics for opea-vocy classifi- cation and segmentation tasks. Each initialized part query P is repeated Np times to accommodate multiple instances of the same part category within single object (e.g., a bus having four wheels). Consequently, the part queries of the same part category and the same object are identical, e.g., pd of busl's 's wheell and bus wheel2. On the other hand, the queries of the same category but different objects are different due to the distinct visual information from the objects, e.g., p of bus Iusl's 's wheell and bus2's wheell. The initialized query is further refined by a MLLM to link the visual and text information between the object and its corresponding part, detailed in the following subsection.
3.5 MLLM-based Parsing
To parse the multi- gulti-granularity concepts embedded in P = (,p), we utilize PaliGemma 2 [49], lightweight and state-of-the-art MLLM that takes the image I, the concatenated object-part queries pô, and prompt guidance as input and implements object-part parsing in our frame- work. From the prompt, the MILM receives the object query 0 followed by part queries p0 and outputs refined part queries that integrate both object- and part-level information. This design enables the MLLM to leverage object-level context infer part semantics, and also allows bidirectional information flow from parts back to the object during training (see Sec. 4.3 Object-Part Synergy). Subsequently, the image tokens from SigLIP 49] and the prompt with queries po are provided to Gemma 2 model in a structured text prompt as follows:
Please do object-part parsing on the image <img>><im__Joees>dddo sokis>></ingg>
For each object, you will be given a lio of object-port queries:
eab) partspart queryl, query 2 on part query n</obj part>,
please implement object-part parsing by refine the queries that con be used for later part category and mosk prodiction.
There are all the condidate object-part queries: object with parts <obj_ partspart_quae queryl. part_query 2.
part query uf </objj part>;
object with parts coh) partspart gquerll part_query 2.
puet_query u2<lobj partz;
This stage processes object-part queries jointly and outputs part queries P integrated with visual information and object context. Note we utilize Gemma as a feed-forward model, instead of utilizing auto-regressive generation to ensure a controlled output structure. P is obtained from the last hidden states of the corresponding input part queries. P will be used as input for separate part decoder with same structure as the object decoder introduced in Sec. 3.3
3.6 Implementation Details
We employ a two-stage training strategy. In the first stage, we train the model with an object instance segmentation loss only:
L Aets Lobj + Abbox
bbox + Amask
Lobj mask
obj is the focal loss 127 the prediction logits so. Lbbox is the L1 loss predicted object
obj
bounding boxes B r. m is the combination of focal loss and dice loss 34 on the predicted object
masks Mo In the second stage, joint object and part segmentation training implemented with losses on both object and part predictions:
I = Adta (Iahi + I.part) + Abbox (Zobj + I ) + >maakk (I.ujj
+ I,part
)
(8)
The loss functions on part segmentation are the same with the ones on object. The parameters of the Swin-L backbone and MaskDINO decoder are initialized with the pre-trained checkpoints from GLEE [18. Following MaskDINO, the hyperparameters are set to Ack 4, Abbox = 2, Amask
5, T. = 9. The number of repeated part queries N, = 3. The training is conducted on 4 H200 GPUs
with batch size of 16.

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PTS-116
+INS
+INS+PART
Parliaggeeet
Method
obj
part
AP
obj
part
ohj
part
AP
obj
part
AP
VLPart
4.5
29.7
PSALM1
31.6
8.27
13.4
48.0
(42.)
(45.))
58.6
(1.()3.3)
21.
79.2
40.1
48.7
PaRGLEE
38.4
9.20
15.6
58.7
(+18))
(+5.)
61.0
9.57
(40.4)
21.0 (+5.))
81.4
41.5
50.4
Lang
8.86
16.7
(+2.5) 22.3 (45.6)
62.8 16.4 (+7.5)
26.7 (+10)
56.9
experiment: PascalPart-116 (training) -> PartImageNet (evaluation) and in-domain experiment: PartImageNet (training) -> PartImageNet (evaluation). We report object-level (obj), part-level (part), and overall (AP) mAP. The best result is in bold and the second best one is in underline, The notations "+INS" and "ARNS+PRTT" indicate additional training dataset for scalability, Green values reflect relative AP gains over the PPS-1 16 baseline; Cyan values reflect relative mAP gains over the PPS-1 baseline. Gray columns shows in-domain performance.
Table 1:
Method
PartImgNet part
+/NS
+/NS+PART
PPS 116
obj
AP
obj
part
AP
obj
part
AP
obj
part
AP
PSALM
8.58
1.87
2,89
20.0
3.33 (+1.5)
5,87 (+3.0)
20.1
3,58 (+1.7)
6.09 (+3.2)
48.7
13.2
18.6
ParGLEE
8.00
2.18
3.06
23.1
3.06
(40.9)
6.17 (+3.1)
22.2
3.37
(+1.2
6.33 (43.3)
53.2
14.5
20.4
LangHIPPS
9.57
2.20
3.32
23.3
3.64 (+1.4)
6.63 (+3.3)
22.6
4.67 (+2.5)
7,39 (+4.1)
54.6
15.0
21.0
Table 2: Cross-dataset experiment: PartImageNet (training) - PPS-116 (evaluation) and in-domain experiment: PPS-116 (training) PPS-116 (evaluation).
4 Experiments
4.1 Cross-dataset Object-Part Instance Segmentation
We conduct experiments to evaluate the cross-dataset generalization performance of LangHOPS, as well as baseline methods for the object-part instance segmentation task.
Experiment Setup. We follow the setup proposed in VLPaat [50] where each method is trained on one base dataset and evaluated on another unseen dataset, without finetuning. Two settings are implemented: Pascal-Part-Ia the model is trained on Pascal-Part-I 16 and evaluated on PartImageNet, and vice versa). We further evaluate the scalability of LOngHOPS by integrating two additional sets of datasets into training, including object-level datasets INS (consisting of COCO (28), VisualGenome (21 and LVIS [14,
(55) + PartImageNet [16] and PartImageNeet - Pascal-Part- 116 (i.c.,
with object annotations) and part-level datasets (PART consisting of ADE20K (67 SA1B 20 and
PACO 42), with object and part annotations). Note the granularity of the part-level annotations across the datasets within PART are different. The metric is m.P maakk on the evaluation set of PartImageNet and Pascal-Parts 116 dataset.
Baseline methods. The existing methods for the OVPIS task include VL.Part 50 and ParGGEE 24 To extend the set of baselines for comparison, we further adapt PSALM (64) a state-of-the-ae LLM- based 2D object-level segmentation method by extending the LLM mask tokens with Icarnable part queries for object-part parsing and part segmentation. The adapted PSALM is denoted as PSALM
Cross-dataset and in-domain evaluation on PartImageNNet. As shown in Tab. LangHOPS achieves the best performance of object-part instance segmentation in both cross-datase and in- domain settings traeed with Pascal-Part- 16 and evaluated on PartImageNet). LangHOPS surpasses PartGLEE by 1.1% and PSALM by 3.3% in mAP abject-part instance segmenta- tion. Our experiments further show that LangHOPS has better scalability with additional training datasets containing part-level annotations. Trained on Pascal-Part- 16+INS, all methods achieve similar performance gains in both part-level mAP and overall AP. However, when the training set is extended with additional part-level datasets (Pascal-Part- + INS + PART) approach achieves significant performance boost in both part-level mAP (+7.5) and overall AP (+10.0). In contrast, the performance gain of PariGLEE in part-level segmentation drops (+5.99 + +5.4) compared to the Pascal-Part-I 16+INS setting, mainly due to lacking the object-part hierarchy context during part parsing phase, as illustrated in Sec. 3.4
Cross-dataset and in-domain evaluation on Pascal-Part-116 As shown in Tab. training the model on PartImageNet and implementing evaluation Pascal-Part- 116 is more challenging than the previous condition for the evaluated methods. Indeed, the latter dateset contains multiple novel object categories and finrrgrraullrrty parts than the former. LangHOPS achieves the best
7

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PSALM
ParGGLEE
LangHOPS
Ground Truth
Bus
Person
Cat
Aeroplane
Figure 3: Qualitative results of part-level segmentation if LangHOPS and baselines.
performance in both cross-dataset and in-domain object-part segmentation on Pascal-Parts 166 The experiments also shows the advantage of LangHOPS in scalability, especially in the setting with PartlmageNet+ INS PART training dataset (+4.1 over +3.2 and +3.3) when trained only on PartImageNet dataset. Here, LangHOPS performs better than both baselines PartGLEE and PSALM
Qualitative Results shown in Fig. in the setting of PartImageNet INS PART -> Pascal- Part-1 16. As the figure shows, LangHOPS achieves more accurate part segmentation than other baselines. Importantly, segmentation results "person" and cat demonstrates HOnS'S superior generalization performance to finer part granularity in the cross-dataset condition.
4.2 Zero-shot Part Segmentation
We further carry out experiments on the OV-Part benchmark 55 and PartImageNet dataset [6 for the zero-shot segmentation. One must note that this benchmark is evaluating open-vocabulary semantic segmentation of parts, which is not the core application of LangHOPS. The metric used is the harmonic mean of intersection-over-niii
(hloU), for both seen and unseen categories [56]:
hIOU 2 2-mloUseer seen mlou unseen
mI sCus + m.lot unseen
As shown in Tab, LangHOPS achieves the best performance on Pascal-Part-l 16 and PartImageNet datasets, and reaches second-bess performance on ADE20K-234 dataset, achieving competitive performance with PartCATSeg [8] LangHOPS obtains the highest m mIoUseee on all three datasets, demonstrating the superior generalization ability unseen object and part categories. Noticeably, our method is designed for open-vocabulary object-pan instance segmentation while most others, including PartCATSeg (8), are designed specifically for the OV-Part benchmark (semantic part segmentation). Directly evaluating LangHOPS in the semantic segmentation still leads to superior performance (hIOU) in PPS-1 and PartImageNet datasets, showing its great potential.
4.3 Ablation Study
Ablation studies further demonstrate the effectiveness of LangHOPS.
Setting
Detached
Obj-Part Seg
obj
Object-Part Synergy. To showcase the object-part synergy enabled by HOPS (i.e., performance improvement from joint train- ing of object and part instance segmentation) reported the our
0.58
0.67
Table 5: Attention score.

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PPS 116 (5S
PartImageNet [6]
ADE20K 55
Method
seen
unseen
kTot!
seen
unseen
hToU/
seen
unseen
KToU/
VLPart
600
42.6
18.7
26.0
ZSSeg+
31
54.4
19.0
28.2
x
x
43.2
27.8
33.9
CLiPSeg
52
55
48.9
27.5
35.2
53.9
37.2
44.0
38.2
30.9
34.2
CAT Sey
55
43.8
27.7
33.9
47.3
35.1
40.3
33.8
25.9
29.3
ParCLIPPSe
[
50.0
31.7
38.8
56.3
51.7
53.9
38.4
38.8
38.6
ParGLEE 24
57.4
27.4
37.1
51.3
35.3
41.8
PartCASSeg
57.5
44.9
50.4
73.8
71.5
72.7
53.1
47.2
50.0
LangHOPS
59.2
46.5
52.1
21.2
73.7
72.8
49.3
49.7
49.5
Table 3: h-loU. Zero-shot evaluation on PPS-116 PartImageNet and ADE20K.
Training selup
Obj Sey
Detached Obj Seg
Obj Part Seg
Eval Dataset
Obj
Part
Obj
Part
Obj
Part
PPS-116
0.00
25.2
9.66
26.2
10.3
PartImageNoo
2.08
62.9
13.2
14.9
Table 4: mAP of object and part instance segmentation. Ablations Object-Part Synergy.
performances in following training setups: a) "Obj Seg": LangHOPS is trained only with the loss of object instance segmentation; b) "Detached Obj-Part Seg": LangHOPS trained using losses of both object and part instance segmentation. However, the gradient flow coming from "MLL.Mbassdd parsing" module interrupted, meaning that the gradients of from the part segmentation loss will not directly propagate to object queries 0 One note that object and part segmentation will still affect each other indirectly since both tasks use the same dense image features X. c) "Obj-Part Seg": This setup allows joint training of object and part instance segmentation without gradient flow cut. As show in Tab. 4 in "Obj Seg" setting, the mAP part segmentation performance is near 0, as the loss of part segmentation is not used. Compared segmentation of "Detached Obj-Part Seg" drops 0.6% on PascalPart 16 dataset and more significantly on PartImageNet dataset by 5.0%. due the absence of the gradient flow by the MLLM- parsing. In contrast, LOng shows improved object segmentation performance in "Obj-Part Seg" than "Obj Seg", and gains significant boost in both object (by 5.4%) and part segmentation (by 1.7%) ) over "Detached Obj-Part Seg". This demonstrates that the proposed MLL.M-aased object-part parsing enables beneficial synergy effect in both cross- cross-dataset and in-dataset conditions. We further investigate the object-part synergy mechanism by reporting the average attention score. The average attention score calculated by summing attention scores of true positive predictions inside the ground truth masks M, divided by the area of the masks. The attention is the normalized cos similarity between object queries and the dense features of the final layer of the object/part decoder.
"Obj Seg", the performance of object
1 cos(furr PM)
S. = Σ
(10)
2 2
REM
where u is the pixel within the ground truth mask M, (fv is the mapped feature for segmentation and Par is the refined object/part query of the predicted instance matched to the ground truth instance. The score shows the amount of attention correctly assigned by the model to the ground truth area, and is in the range of [0. 1]. In the setting of PPS116 PPSNSARTSTP -> PartImageNet, as shown in Tab. []] compared to the "detached object-part seg.", the synergized object- -prt segmentation leads to higher attention scores for both object and part segmentation, proving strong evidence of the synergy between both segmentation tasks.
Effect of MLLM-based Parsing. We implement ablation study to demonstrate the effectiveness of the MLLM-based Object-Part Parsing module by replacing it with a Q-Former. The Q-Former takes the object queries O key and value, and hierarchical part queries Pô query. In the end, the Q-former-based module outputs part queries PQ for part segmentation purposes. As shown in Tab, 7 the ablated version, denoted as "w/o MLLM" shows inferior performances with both PartImageNet and PPS 1 16 datasets, demonstrating the effectiveness of the MLLM module in object-pan part parsing.
Ablation on two-stage. We also provide an ablation study on the training strategy of the model. Two-stage refers to firstly training the model on object segmentation and secondly training it on object-part segmentation. One-stage means we directly train the model on object-part segmentation

---

PPS-116
+INS
+ANS+PART
PartImageNet
Method
obj
part
AP
obj
part
AP
obj
part
AP
obj
part AP
One-Stage
40.6
8.50
15.6
57.8
10.6
21.1
60.2
15.5
25.4
84.6
51.2
58.6
Two-Stage
44.5
8.86
16.7
60.5
11.4
22.3
62.8
16.4
26.7
83.9
49.2 56.9
Table Ablations on training strategy in the cross-dataset setting of PascalPart-116 (training) ParllmagNNet (evaluation).
from scratch. Tab. 6Jshows the model trained with the two-stage strategy achieves better cross-dataset performance, though its in-domain performance is inferior compared to one-stage.
Effect of Language-grounded gate the effectiveness of the Hierarchies. To investi- object- part hicrarchies, we conduct an ablation study by replacing the representation proposed in Sec. 3.5 with N learnable queries, denoted as "w/o hierarchy" in Tab. 7 Specifically, initialize N learnable queries and concatenate them to each object query o from O to form the initial part queries PÑ, Subsequently, the MLLM uses the 04, P. and as input, and outputs parsed part queries that are for- warded the part decoder for final part segmentation. As shown in Tab. 7 by leveraging hierarchies between object and parts, and formulating part queries within language space, LangHOPS achieves better performance than the ablated version with learnable initial queries in both datasets.
Module
PartImageNet
PPS116
w/o MLLM
23.2
18.4
hierarchy
19.1
LangHOPS
26.7
19.8
Table 7: mAP object-part instance segmentation in the cross-dataset setting. Ablations on architecture design.
4.4 Limitation and Future Work
As shown in the supplementary material (Section A.4), the computational cost of LangHOPS is nontrivial compared to the baselines. primarily due the integration of the MLLM for object-part parsing. Improving efficiency is essential for deploying LangHOPS in real-time or on-board computer vision and robotics applications. mainly contain common object and part categories, which may not fully cover all potential application scenarios. Therefore, additional datasets with task-specific annotations may still be required for fine-tuning in specialized cases (e.g.. interactable articulated objects for robotic manipulation), even though LangHOPS demonstrates strong generalization capabilities compared to existing baselines. Additionally, as 2D-to-3D lifting 32
addition, the training datasets
16 21 28 used in this work
40
48
59 is increasingly popular, leveraging LangHOPS for
3D computer vision tasks [15
60
61
also a promising future direction.
5 Conclusion
We propose a new method LangHOPS that performs Open-vocabulary Part Instance Segmentation through hierarchical modeling in language space. Using language-grounded hierarchies improves both the context awareness and the accuracy of object-part parsing. In experiments, we show that LangHOPS performs notably better than existing state-of-the-art methods across multiple benchmark settings. Notably, our method achieves significant improvements in in-domain and cross-dataset object-part instance segmentation, where outperform existing state-of-the-art approaches by 5.5% AP. LangHOPS further achieves the best m OUU on unseen object-parts in OVPIS tasks, on all PartImageNet. PascalPart- and ADE20K datasets, consequently demonstrating strong generalization ability in unseen object and part categories. In conclusion. LangHOPS establishes novel foundation for Open-vocabulary Part Segmentation and highlights the potential of MLLM- based methods for fine-graine visual understanding, with the aim of encouraging further research into scalable language-driven approaches for structured scene parsing.
6
Acknowledgement
This research was funded by Toyota Motor Europe and the Ministry of Education and Science of Bulgaria (support for INSAIT, part of the Bulgarian National Roadmap for Research Infrastructure).
10

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A Technical Appendices and Supplementary Material
This section provides additional visualization and ablation studies.
A.1 Visualization
PartimageNet Annotations
Pascs/Part116 Annotations
Figure Visualization on Annotations of PartImageNet and PascalPart datasets.
Granularity Difference Across Dataset. In Fig. provide additional visualizations of the an- notations of PartImageNet and PascalPartl 16 datasets. The figure shows that the two datasets provide annotations of parts in different granularity. Generally, PascalPartl 16 has finer part definition and thus is more challenging to implement part segmentation on PascalPart 16 than on PartImageNet, which explains that both cross-dataset and in-domain settings, HOnS and baselines achieve less mAP on PascalPart1 16 than on PartImageNet.
Failure Cases. We further provide failure cases of LangHOPS in the cross-dataset setting. As shown in Fig. LangHOPS can fail in several cases:
when the object distant to the camera and has small area in the image, LangHOPS may not be able to detect all the parts (one motorbike's 's wheel missing);
in the cross-dataset setting, LangHOPS have difficulties in generalizing to some novel parts which has not see during training (bird's ss eye, cat's eye). As shown Fig. 4 the training dataset (Partlmagenet) only contain annotations of animal's 's head and no annotation of eyes.
when the training and evaluation dataset have different annotation styles, the trained model tends predict the part segmentation in the style of training dataset (bicycle's 's wheel, all the pixels within the wheel circle).
A.2 Robustness Analysis
Statistical Robustness of Evaluation. We conduct repetitive experiments the same in Sec. 4.1 with
3 different random seeds. The average and standard deviation are calculated and reported in Tab. The table shows the statistical stability of the cross-dataset evaluation and verifies the superiority of the proposed LOng over the baseline.
Robustness to Prompt Formulation. We conducted two ablation studies on the ordering and wording of the structured input prompts assess the robustness of our method prompt formulation. (a) robustness to prompt ordering: We randomly shuffled (i) the order of object queries, and (ii) the order of part queries within each object, multiple times during inference. For instance, object 3 may appear before object 1, or part queries within an object may be permuted (e.g., "part 9, part 4, part 6"). As shown Tab. g our method remains highly stable across these permutations, with minimal performance degradation, demonstrating robustness to input ordering. (b) robustness to wording: We further test the model's robustness to unseen part names by replacing the subset (from to 100%) of the original part category names with GPT-4o-generatee synonyms (c.g., "foot" "leg").
14

---

Predicted Parts
G.T. Parts
Figure 5: Failure cases of LOng in the cross-dataset setting of PartImageNet-/NS-PARTT (train- ing) - PPS-I16(cvaluation).
Method
PPS-116
+INS
+INS+PART
obj
AP
part
obj
part
AP
38.4+0.5
15.240.5
57.641.8
21.5+0.6
10.5+0.7 16.640.3
21.5=0.7
LangHOPS
48.7+3.3
8.89+0.24
17.7+0.9
60.9+0.7
12.1+1.0 22.9#1.0
63.7+1.4
27.0=0.5
(a) PPS-116 116 + PartImageNet
PPS-116
Method
obj
part
AP
obj
part
AP
obj
AP
PartGIFFE
8.53+0.52
2.05=0.09
3.0440.16
23.3=0.1 22.9=0.7
3.10+0.16 6.17#0.16
22.5+0.4
3.60+0.24 24
6.46#0.26
LangHOPS
11.0=1.0
2.17=0.03
3.50
3.6820.11 1I 6.5920.19
23.2+0.5
4.51
7.3420.28
(b) PartlmageNet PPS-116
Table 8: Evaluation of PartGLEE and LangHOPS with mean = standard deviation over 3 runs with different random seeds in the cross-dataset settings.
As shown in Tab. 10 LangHOPS significantly outperforms ParGLEEE under increasing synonym replacement ratios, indicating strong generalization to semantically similar but unseen phrasing. Note that synonym substitutions may introduce granularity mismatches with the dataset's ground-truth annotations (e.g.. "leg" may exclude "paw" in the ground truth for "foot"), which partially explains the observed performance drop.
Robustness to Noisy Hierarchy. We test the common OVS setting using clean object-part hierarchies, but believe in the value of closing the gap towards a noisy real-world deployment. To evaluate the robustness of LOngHOPS to noisy or automatically mined hierarchies, replace portion of the clean object-part taxonomy with GPT--4o-eenerated object-part hierarchies. These auto-mined hierarchies are constructed solely from the object category names and may introduce ambiguity, inconsistency, or irrelevant parts. In Tab, II we report performance under the varying noise hierarchies as the input prompt while the remaining the clean dataset annotations for evaluation. We observe that: LangHOPS consistently outperforms PartGLEE across all noise levels; LangHOPS degrades more gracefully noise increases, maintaining reasonable AP even when the hierarchies are noisy; The performance gap widens especially at high noise levels, demonstrating LangHOPS's stronger resilience to imperfect or automatically mined hierarchies. Please note that the auto-generated hierarchies are often inconsistent with the ground truth annotations in the dataset, leading to lower evaluation metrics. Overall, developing evaluation protocols for adaptive, fask-specific hierarchies remains open problem and promising direction for future benchmark design.
15

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Method
PPS-116
+INS
+INS+PART
obj
part
AP
obi
AP
obj
part
AP
Shuffling of Object
47.8:22.7
8,57:0.33
17.1=009
61.1208
11.7+0.8
22.6+0.8
63. 03.1
15.820.3
26,7::..4
Shuffling of Part
46.9:22.4
9.08:+0.33
11.5:0.88
58.811.1
13.6+0.9
23.6#0.9
64.2
16.900.3
27.4:006
No shufllng
48.7.13.3
8.89:10.24
17.71009
60.910.7
12.141.0
22.9:0..7
63.74:14
16.6.0.3
27.0.10.5
Table 9: Ablations on the ordering of the object and part queries
PPSI 16 Part-aaarelmaeeeetttt
Method
0%
25%
50%
75%
100%
part
AP
part
AP
part
AP
purt
AP
part
AP
PanGLEE
11.2
21.8
9.3
20.3
8.6
19.7
6.6
18.2
5.1
17.0
LangHIPPS
17.0 27.1
16.2 26.5
16.5
26.7
14.6
25.3
12.7
23.8
Table 10: Ablation the robustness to input part category names. PPS116 + INS + PART - PartImageNet. Different percentages of part category names replaced with GPT-4o generated synonyms.
A.3 Additional Ablation study
Ablation on Nyp- We further provide ablation study on the number of repeated part queries for each object Np in the cross-dataset setting of PPS- OPS-L16+/NS+PARY (training) PartImageNet (evaluation). As shown in Tab. [2] the object-part segmentation performance drops when the N, is too small (1, 2) too large (4, 6),
Ablation on backbone finetuning. We further conduct an ablation study to show the necessity of finetuning the visual backbone and pixel decoder during training. As we can in the Tab. [3] finetuning the visual backbone and pixel decoder leads to improved performance especially in the part segmentation task. This effect is mainly due the fact that the used visual backhone and pixel 66 (23) are pretrained only on object-level tasks, and the extracted dense features lack part-level understanding. Thus, finetuning them the object-part-level tasks is beneficial.
A.4 Computation Cost
We report the footprint GPU hours, carbon cost, inference cost and model size of PartGLEE. PSALM and LangHOPS. The gpu hours and inference time reported with Nvidia H200 GPU(s). The spec. power (700W) of H200 and world average carbon intensity of electricity (0.475 kgCO2/kW1
are used for calculating the footprint. The Tab. 14 shows that LangHOPS has the largest model size, mainly due to the usage MILMM (Paligemma2-3B PSALM* has the longest training time and carbon footprint since it trains the LLM instead of using LoRA, and needs to process all candidate category names, which leads to long input prompts to the LLM. LangHOPS achieves the best performance with reasonable training and inference cost compared the baselines.
16

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Method
0%
25%
50%
75%
100%
part
AP
part
AP
part
AP
part
AP
part
AP
PanGLEE
11.2
21.8
10.3
21.1
20.7
8.2
19.4
3.6
15.8
LangHOPS
17.0
27.1
13.1
24.1
1244
23.5
8.8
20.7
6.7
19.1
Table 11: Ablations on the noisy hierarchy construction. Different percentages of obj-part hierarchies from the dataset are replaced with GPT-4o generated ones.
Np
2
3
4
5
6
Obj AP
61.7
62.1
62.8
62.4
61.4
61.8
Part AP
15.4
15.8
16.4
16.0
16.1
15.9
Table 12: Ablation Study on Np.
PPS-116
+INS
+INS+PART
PattImageNet
Method
part
AP
obj
part
obj
part
part
AP
Frozen
6.99
16.1
64.1
Prozen Bk
47.6
7.36
16.3
63.0
6,98
19.4
63.4
12.4
23.8
83.2
34.7
45.4
LangHOPS
49.1
8.62
17.6
61.8
13.6
24.3
62.7
17.0
27.1
85.5
47.9
55.8
(a) (PPS-116 116 PartImageNee.
PartImageNet
+INS
+INS+PART
PPS-116
obj
part
AP
obj
part
AP
obj
part
AP
obj
part
AP
Prozen Bk+Pd
10.5
1.71
3.05
23.4
2,57
5.73
23.2
2.76
5.86
53.3
7.48
17.7
Prozen Bk
11.8
1.95
3,45
23.3
2.92
23.0
3,34
6.32
44.2
7.65
15.8
LanglTOPS
11.3
2.17
3,47
21.9
3,82
6.55
23.8
4.16
7.13
56.4
15.3
21.4
(b) PartImageNet -> PPS-116.
Table 13: Ablations on frozen image backbones and pixel decoder in the cross-dataset settings. "BK" refers to the visual encoder and "Pd" refers to the pixel decoder in the Fig. 2
Method
Model Size
Training GPU Hours
Training Footprint (kg CO.e)
Inference Time (ms)
PSALM!
1.5B
92
30.6
628
PanGLEE
IB
40
13.3
240
LangHOPS
4B
72
23.9
396
Table 14: Computation Cost of LangHOPS and the baselines. PPS116 INS + PART - PartIma-
geNet.
17

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Reviews:
[{"review_id": "g1f8bOqwpa", "reviewer": "Reviewer_thRp", "summary": "This paper introduces an object-level instance and object-specific part-level instance segmentation method. The results on several datasets surpass previous methods.", "questions": "1.  Line 42-47, can you explain the limitations of previous methods? I do not entirely agree with your points.\n2. Why do the authors train the visual and pixel backbones, which is very rare? \n3. What is the difference between the proposed method and a set of learnable, universal queries in PartGLEE in Section 3.4?", "limitations": "Some parts are insufficiently explained in section 3.2.\nThe improvements in Tables 2 and 3 are not significant.", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nThis paper provides a detailed definition of the problem associated with this \"new\" task. The experiments conducted on different benchmarks verify the effeteness of the proposed method, particularly in the cross-dataset experiment.\n\nWeaknesses:\n\nHowever, the motivation for this task is not explained well. The object-specific part-level instance segmentation is more like a combination of part-segmentation and instance-segmentation tasks. It is hard for readers to get the motivation in the beginning.\n\nIn addition, the proposed method includes several components while the connection is not strong. The part segmentation module and the object segmentation have overlaps for a specific object category. For instance, both parts can learn the information for \"bus\". They are no relations with this information when building the structure. \n\nAnother critical issue is why the authors refer to it as language-grounded. The processing of the textual part is limited. The prompts proved from neither object segmentation nor part segmentation are passed through a frozen text-encoder, which is followed by a concatenation. The additional information plays limited role as \"language-grounded\".\n\nThe paper requires careful proofreading, particularly in image captions and Section 3.2, 4.3.", "was_revised": true, "final_justification": "The authors provide a rebuttal to address some concerns from the first round. However, some key points (e.g., motivation, the distinction from object/part segmentation) requires more sufficient explanations and experiments. Therefore, I keep my initial rating as borderline reject.", "rebuttal": "We appreciate the reviewer's comments and recognition of performance gains achieved by our method on different benchmarks, especially on open vocabulary cross-dataset tasks, which are a central target of our method.\n\n### 1. Motivation Clarification\nWe do not perform object-specific part segmentation, but rather segment parts of all requested objects simultaneously, following the broadly accepted **open-vocabulary part segmentation (OVPS)** framework.\n\nFor the relationship between *part- and instance-segmentation*, in fact, prior work such as **PartGLEE** follows the approach mentioned by the reviewer — simply combining object and part segmentation modules via a Q-Former.  \n\nIn contrast, our method models the **object-part hierarchy** in **language space**, leading to significant performance gains over PartGLEE. It is significant to address the challenge:\n> While object-level instance segmentation is well-defined, part-instance segmentation is task-dependent and inherently requires open-vocabulary capabilities.\n\nFor example:\n- Using a laptop may require segmenting the **lid** to open it.\n- Repairing it may require finer segmentation of **screws** or **hinges**.\n\nWe solve it by:\n- Incorporating open-vocabulary prompts into object segmentation;\n- Modeling hierarchical object-part relationships with structured prompts;\n- Enforcing a one-to-one mapping between object parts and part segmentation queries;\n- Using language-grounded queries in the part decoder that leverage the reasoning capabilities of an MLLM.\n\nWe will clarify this in the introduction.\n\n### 2. Connection Between Modules \nWe believe this is a misunderstanding.\n\nOur object and part segmentation modules are tightly coupled via the hierarchical object-part representation (mentioned in Line 140, 174, and Figure 2) and aim at utilizing the joint information between parts and objects through the following information flow:\n- For each segmented object, a set of part queries is constructed by a concatenation of the object embedding $\\mathbf{O^H}$ generated in the object decoder and the language-encoded part description.\n- These queries are processed by the MLLM, generating queries that are capable of open-vocabulary part segmentation.\n- The processed queries are used in the part decoder to segment each requested part.\n\nOur ablation study in l269ff and Table 5 shows a comparison with detached object-part segmentation that demonstrates that the joint training with information flow from the part decoder to the object decoder is efficient and improves performance.\n\nWe will add this paragraph to the architecture overview.\n\n### 3. Language Grounding\nWe respectfully disagree and refer to our method as being language-grounded since we design the whole object-part representation and processing to remain in a language-aligned space. Our motivation to follow this approach is to utilize the generalist language representations learned by the MLLM. \n\nIn detail the language-grounding is enabled in the following way:\n- Both object $\\mathbf{O^H}$ and part queries $\\mathbf{P}$ generated in the segmentation modules are aligned to the language space and compared against text embeddings of category labels for classification.\n- More critically, language processing in our method goes beyond the frozen text encoder. We combine the hierarchical part structure in language space with semantic object embeddings produced by the object segmentation, explicitly modeling object-part hierarchies in language space.\n- We leverage these text-aligned embeddings in a pretrained MLLM, which enriches the queries with contextual and semantic knowledge from the vision-language domain.\nSimilarly, the output of the MLLM resides in a text-informed feature space, allowing the part decoder to extract visual features in a way that is conditioned on open-vocabulary semantics.\n\nThis design enables the open-set generalization capability and allows the model to process novel categories and part-object relationships.\n\n### 4. Limitation of Previous Methods\nWe clarify the limitations of the existing methods. It is recognized that CLIP’s embeddings have limited capacity for compositional understanding and modeling object-part hierarchies[1, 38, 48]. As a result, CLIP-based methods such as OV-Part [42] and PartCLIPSeg[7] tend to show suboptimal performance in fine-grained part segmentation. To address this, PartCatSeg augments the model with DINOv2 for structural guidance . However, the approach still lacks an explicit mechanism for modeling object-part hierarchies grounded in language, which is critical for compositional generalization.\n\nFor another existing work, PartGLEE [20], it has inherent limitation by design in handling part granularity variation across datasets. It parses objects into parts through a learning-from-scratch Q-Former, which is incapable of handling object-part granularity unseen during training. For example, trained with fine-grained annotations like \"eye\", \"nose\", \"ear\" for the cat on Pascal-Part-116, PartGLEE performs poorly when evaluated on PartImageNet, where the cat is annotated with the only coarser parts (\"head\", \"body\", \"foot\", and \"tail\"). \n\nWe will revise the part mentioned by the reviewer to make it clear. \n\n### 5. Training of Visual Backbones and Pixel Decoder\nIt is indeed necessary to train the visual backbone and pixel decoder. The visual backbone is from Mask2Former CVPR'22, pretrained only in the object-level segmentation task and thus, is limited in segmenting fine-grained, part-level instances. Therefore, we train the backbone on part-level datasets to grant it part-aware segmentation ability. This practice is actually not uncommon in the literature on **part-level segmentation** (e.g., [20, 40]).\n\nWe further conducted an ablation study on the freezing/training of the visual backbone and the pixel decoder. As shown in Table R-1 and R-2, training the visual backbone and the pixel decoders leads to better performance in part-level segmentation and higher overall AP.\n\n| Method|PPS-116|-|-| +INS | -| - | +INS+PART |-| -| PartImageNet|-| -|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|                      | obj | part | AP       | obj | part | AP       | obj | part | AP         | obj | part | AP           |\n| Frozen Bk+Pd         | 48.2| 6.99 | 16.1     | 64.1| 8.85 | 21.1     | 66.1| 9.34 | 22.0       | 80.6| 30.1 | 41.3         |\n| Frozen Bk            | 47.6| 7.36 | 16.3     | 63.0| 6.98 | 19.4     | 63.4| 12.4 | 23.8       | 83.2| 34.7 | 45.4         |\n| LangHOPS             | 49.1| 8.62 | 17.6     | 61.8| 13.6 | 24.3     | 62.7| 17.0 | 27.1       | 85.5| 47.9 | 55.8         |\n\n**Table R-1:** Ablations on freezing backbone (Bk) and pixel decoder (Pd). Cross-dataset and in-domain evaluation, PPS-116 -> PartImageNet (with Random seed 4337).\n\n| Method|PartImageNet|-|-| +INS | -| - | +INS+PART |-| -| PPS-116|-| -|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|                       | obj  | part | AP       | obj  | part | AP       | obj  | part | AP         | obj  | part | AP           |\n| Frozen Bk+Pd          | 10.5 | 1.71 | 3.05     | 23.4 | 2.57 | 5.73     | 23.2 | 2.76 | 5.86       | 53.3 | 7.48 | 17.7         |\n| Frozen Bk             | 11.8 | 1.95 | 3.45     | 23.3 | 2.92 | 6.01     | 23.0 | 3.34 | 6.32       | 44.2 | 7.65 | 15.8         |\n| LangHOPS              | 11.3 | 2.17 | 3.47     | 21.9 | 3.82 | 6.55     | 23.8 | 4.16 | 7.13       | 56.4 | 15.3 | 21.4         |\n\n**Table R-2:** Ablations on freezing backbone and pixel decoder. PartImageNet -> PPS-116.\n\n### 6. Differentiation from PartGLEE\nLanguage-Space Object-Part Representation, a core innovation of our method, distinguishes ours from PartGLEE. Unlike PartGLEE, which uses a fixed set of learnable part queries processed by a Q-Former, our method dynamically constructs part queries in language space, conditioned on both the object context and a user-defined list of candidate part categories. \n\nFor each object query from the object segmentor, we iterate over candidate part labels and concatenate each text embedding with the object query to form context-aware initial part queries. These are then further refined by a multimodal LLM in Section 3.5. To address the reviewer's concern, we conduct an ablation study by replacing the language-grounded part query initialization with fixed learnable tokens as in PartGLEE and use them as the input to the MLLM, which drops the cross-dataset performance significantly compared to the proposed method:\n\n| Method|PPS-116|-|-| +INS | -| - | +INS+PART |-| -| PartImageNet|-| -|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|               | obj  | part | AP      | obj  | part | AP       | obj  | part | AP        | obj  | part | AP            |\n| w. LQ| 46.8 | 8.10 | 16.7    | 58.2 | 11.8 | 22.1     | 58.9 | 13.9 | 23.9      | 81.3 | 46.7 | 54.4          |\n| LangHOPS      | 49.1 | 8.62 | 17.6    | 61.8 | 13.6 | 24.3     | 62.7 | 17.0 | 27.1      | 85.5 | 47.9 | 55.8          |\n\n**Table R-3:** Ablations on Learnable Query. Cross-dataset and in-domain evaluation, PPS-116 -> PartImageNet (with Random seed 4337). LangHOPS w. LQ means that the \"Language-Space Object-Part Representation\" is replaced by the PartGLEE-like learnable queries concatenated with object queries.\n\n| Method|PartImageNet|-|-| +INS | -| - | +INS+PART |-| -| PPS-116|-| -|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|| obj  | part | AP      | obj  | part | AP       | obj  | part | AP        | obj  | part | AP           |\n| w. LQ | 11.0 | 1.95 | 3.32    | 28.6 | 3.02 | 6.54     | 22.7 | 4.26 | 7.05      | 54.6 | 9.65 | 19.6         |\n| LangHOPS       | 11.3 | 2.17 | 3.47    | 21.9 | 3.82 | 6.55     | 23.8 | 4.16 | 7.13      | 56.4 | 15.3 | 21.4         |\n\n**Table R-4:** Ablations on Learnable Query. PartImageNet -> PPS-116."}, {"review_id": "MQB7t9TtCZ", "reviewer": "Reviewer_PJs6", "summary": "The paper introduces LangHOPS, the first framework that tackles open-vocabulary object–part instance segmentation (OVPIS) by (i) explicitly encoding object–part hierarchies in CLIP language space and (ii) passing these language-grounded queries through a lightweight MLLM (PaliGemma-2) to refine part queries before decoding. Experiments on three benchmarks (PartImageNet, PascalPart-116, ADE20K) show consistent state-of-the-art gains: +5.5 AP (in-domain) and +4.8 AP (cross-dataset) on PartImageNet, and +2.5 mIoU on unseen ADE20K parts. Ablations confirm that both the language-grounded hierarchy and the MLLM parser are indispensable and that joint training yields positive object–part synergy.", "questions": "1). Statistical robustness – Please report confidence intervals (e.g., ± std over three seeds) for main tables. A ≥ 3 pt drop might change my rating.\n2). Prompt sensitivity – How stable is performance to wording or ordering of the structured prompt? A study on a held-out set would strengthen claims of robustness.\n3). Compute footprint – Provide GPU hours and carbon cost for each training stage and for the large INS + PART run; if substantially larger than baselines, discuss trade-offs.\n4). Hierarchy construction – The method assumes a clean object→part taxonomy. How does LangHOPS behave when candidate lists are noisy or overlapping? An experiment with automatically mined candidates would be helpful.\n5). Synergy mechanism – The paper shows gains but not why gradients from parts help objects. Visualizing attention maps before/after joint training could clarify this; stronger evidence could raise my score.", "limitations": "The paper openly notes that synergy is implicit and that the role of language-guided hierarchies on object quality is not fully characterized (Sec. 4.4). However, potential societal impacts (e.g., misuse of fine-grained segmentation for surveillance) are not discussed; consider adding a Broader-Impacts paragraph.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- Strengths:\n\n  1). Solid empirical evidence: thorough in-domain, cross-dataset, zero-shot, scalability and ablation studies.\n\n  2). Clear Implementation details: two-stage training, clear loss formulations and hyper-parameters.\n\n  3). Significance and originality: First to integrate an MLLM for OVPIS; shows that language-grounded hierarchies boost both part and object quality; could inspire similar hierarchical designs in other dense tasks. Combines hierarchical language modelling with MLLM-driven query refinement; introduces explicit synergy analysis.\n\n\n- Weaknesses\n\n  1). Error bars / statistical significance are missing; some ablations (e.g., prompt sensitivity, MLLM size) are limited; compute cost for full training set (INS + PART) not fully quantified.\n\n  2). Impact depends on availability of strong MLLMs; benefit over strong vision-only baselines (e.g., clipped Q-Former) is moderate on ADE20K (49.5 hIoU vs 50.0 of PartCatSeg).\n\n  3). Repetition/typos (e.g., duplicated “Object Segmentation” heading), table captions compressed, and some notation (Np, H) introduced late.", "was_revised": true, "final_justification": "The rebuttal resolved my main robustness and cost doubts and provided new evidence of hierarchy tolerance and part-object synergy. The method remains the first to integrate an MLLM into OVPIS and delivers consistent SOTA across three datasets with solid ablations. Remaining weaknesses are either incremental (ADE20K gap) or addressable in the final version (impact statement). Overall, reasons to accept now clearly outweigh reasons to reject.", "rebuttal": "We thank the reviewer for the highly constructive comments and appreciate the recognition of significance and originality, together with highlighting solid empirical evidence in different settings and clear implementation details. We answer to the reviewer's comments as follows.\n\n### 1. Error Bars\nWe report mean ± standard deviation results over three random seeds (20, 43, and 4337) for both LangHOPS and PartGlee, the strongest baseline. \n\nAs shown in Tables R-5 and R-6, the averaged AP closely align with the originally reported numbers, slightly increasing in most experiments for our method. The standard deviations have a low magnitude, indicating that our results are stable and show a significant improvement over the PartGlee baseline. \n\n| Method|PPS-116|-|-| +INS | -| - | +INS+PART |-| -| PartImageNet|-| -|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|| obj  | part | AP| obj  | part | AP | obj| part | AP | obj  | part | AP|\n| PartGlee ± | 38.4±0.5 | 8.61±0.46 | 15.2±0.5 | 57.6±1.8 | 11.3±0.2 | 21.5±0.6 | 60.1±0.9 | 10.5±0.7 | 21.5±0.7 | 82.0±0.5 | 42.5±0.8 | 51.3±0.7 |\n| LangHOPS±| 48.7±3.3|8.89±0.24 | 17.7±0.9 | 60.9±0.7|12.1±1.0|22.9±1.0 | 63.7±1.4 | 16.6±0.3 | 27.0±0.5 | 84.1±1.0 |48.2±0.8| 56.1±0.8 |\n\n**Table R-5:** PPS116→PartImageNet with standard deviation (±) of the proposed method LangHOPS± and the strongest baseline PartGlee±.\n\n| Method|PartImageNet|-|-|+INS| -|-|+INS+PART |-| -|PPS-116|-| -|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| | obj | part | AP  | obj |part | AP| obj | part | AP| obj | part | AP  |\n| PartGlee ± | 8.53±0.52 | 2.05±0.09 | 3.04±0.16 | 23.3±0.1 | 3.10±0.16 | 6.17±0.16 | 22.5±0.4 | 3.60±0.24 | 6.46±0.26 | 53.6±1.5 | 13.8±0.6 | 19.9±0.7 |\n| LangHOPS ±| 11.0±1.0 | 2.17±0.03 | 3.50±0.18 | 22.9±0.7 | 3.68±0.11 | 6.59±0.19 | 23.2±0.5 | 4.51±0.25 | 7.34±0.28| 55.0±1.1 | 15.0±0.2 | 21.1±0.3 |\n\n**Table R-6:** PartImageNet→PPS116 with standard deviation.\n\n### 2. Prompt Sensitivity\nWe conducted two ablation studies on the ordering and wording of the structured input prompts to assess the robustness of our method to prompt formulation.\n\n**Robustness to prompt ordering**: \nWe randomly shuffled (i) the order of object queries, and (ii) the order of part queries within each object, multiple times during inference. For instance, object 3 may appear before object 1, or part queries within an object may be permuted (e.g., \"part 9, part 4, part 6\"). As shown in Table R-7, our method remains highly stable across these permutations, with minimal performance degradation, demonstrating robustness to input ordering.\n\n| Method|PPS-116|-|-| +INS | -| - | +INS+PART |-| -| PartImageNet|-| -|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| | obj | part | AP  | obj |part | AP| obj | part | AP| obj | part | AP  |\n| Shuffling of Object    | 47.8±2.7 | 8.57±0.36 | 17.1±0.9   | 61.1±0.8 | 11.7±0.8 | 22.6±0.8    | 65.1±0.9 | 15.8±0.3 | 26.7±0.4      | 82.1±1.6 | 47.7±1.2 | 55.3±1.3      |\n| Shuffling of Part| 46.9±2.4 | 9.08±0.33 | 17.5±0.8   | 58.8±1.1 | 13.6±0.9 | 23.6±0.9    | 64.2±1.6 | 16.9±0.3 | 27.4±0.6| 81.7±1.0 | 46.2±0.6 | 54.1±0.7      |\n| No shuffling  | 48.7±3.3 | 8.89±0.24 | 17.7±0.9   | 60.9±0.7 | 12.1±1.0 | 22.9±0.97   | 63.7±1.4 | 16.6±0.3 | 27.0±0.5| 84.1±1.0 | 48.2±0.8 | 56.1±0.8      |\n\n**Table R-7:** Ablations on the ordering of the object and part queries - PPS116->PartImageNet.\n\n**Robustness to wording**: We further test the model's robustness to unseen part names by replacing the subset (from $0\\%$ to 100%) of the original part category names with GPT-4o-generated synonyms (e.g., \"foot\" → \"leg\"). As shown in Table R-8, LangHOPS significantly outperforms PartGlee under increasing synonym replacement ratios, indicating strong generalization to semantically similar but unseen phrasing. Note that synonym substitutions may introduce granularity mismatches with the dataset’s ground-truth annotations (e.g., \"leg\" may exclude \"paw\" in the ground truth for “foot”), which partially explains the observed performance drop.\n\n| Method| 0%|-|25%|-|50%|-|75%|-|100%|-|\n|-|-|-|-|-|-|-|-|-|-|-|\n|| part| AP| part| AP| part| AP| part| AP| part | AP |\n| PartGLEE| 11.2| 21.8| 9.3| 20.3| 8.6| 19.7   | 6.6| 18.2| 5.1| 17.0|\n| LangHOPS | 17.0| 27.1| 16.2| 26.5   | 16.5| 26.7| 14.6| 25.3| 12.7| 23.8   |\n\n**Table R-8:** Ablation on the robustness to input part category names. PPS116 + INS + PART -> PartImageNet. Different percentages of part category names replaced with GPT-4o generated synonyms.\n\nTogether, these studies demonstrate that LangHOPS is robust to both the structure and vocabulary of the input prompt.\n\n### 3. Compute Footprint\nWe now report the footprint of GPU hours, carbon cost, inference cost and model size of PartGLEE, PSALM and LangHOPS in Table R-9. The gpu hours and inference time are reported with Nvidia H200 GPU(s). The spec. power (700W) of H200 and world average carbon intensity of electricity (0.475 kg CO₂e / kWh) are used for calculating the footprint.\n\n| Method | Model Size | Training GPU Hours  | Training Footprint (kg CO₂e) | Inference Time (ms) |\n|-|-|-|-|-|\n| PSALM†| 1.5B|  92| 30.6 | 628  |\n| PartGLEE | 1B | 40| 13.3 | 240 |\n| LangHOPS | 4B | 72 | 23.9 | 396|\n\n**Table R-9:** Specs of LangHOPS and the baselines. PPS116 + INS + PART -> PartImageNet.\n\nThe Table shows that LangHOPS has the largest model size, mainly due to the usage of MLLM (Paligemma2-3B). PSALM† has the longest training time and carbon footprint since it trains the LLM instead of using LoRA, and needs to process all candidate category names, which leads to long input prompts to the LLM. LangHOPS achieves the best performance with reasonable training and inference cost compared to the baselines.\n\n\n### 4. Robustness to Noisy Hierarchy\nWe test on the common OVS setting using clean object-part hierarchies, but believe in the value of closing the gap towards a noisy real-world deployment. To evaluate the robustness of LangHOPS to noisy or automatically mined hierarchies, we replace a portion of the clean object-part taxonomy with GPT-4o-generated object-part hierarchies. These auto-mined hierarchies are constructed solely from the object category names and may introduce ambiguity, inconsistency, or irrelevant parts. In Table R-10, we report performance under varying noise levels: for example, at $25\\%$, $25\\%$ of the object categories use noisy hierarchies, while the remaining $75\\%$ use the clean dataset annotations. We observe that:\n1. LangHOPS consistently outperforms PartGlee across all noise levels; \n2. LangHOPS  degrades more gracefully as noise increases, maintaining reasonable AP even when $100\\%$ of hierarchies are noisy;\n3. The performance gap widens especially at high noise levels, demonstrating LangHOPS's stronger resilience to imperfect or automatically mined hierarchies.\n\nPlease note that the auto-generated hierarchies are often inconsistent with the ground truth annotations in the dataset, leading to lower evaluation metrics. Overall, we agree with the reviewer that developing evaluation protocols for adaptive, task-specific hierarchies remains an open problem and a promising direction for future benchmark design. \n\n| Method| 0%|-|25%|-|50%|-|75%|-|100%|-|\n|-|-|-|-|-|-|-|-|-|-|-|\n|| part| AP| part| AP| part| AP| part| AP| part | AP |\n| PartGLEE    | 11.2   | 21.8   | 10.3   | 21.1   | 9.8    | 20.7   | 8.2    | 19.4   | 3.6    | 15.8   |\n| LangHOPS    | 17.0   | 27.1   | 13.1   | 24.1   | 12.4   | 23.5   | 8.8    | 20.7   | 6.7    | 19.1   |\n\n**Table R-10:** Ablations on the noisy hierarchy construction. Different percentages of obj-part hierarchies from the dataset are replaced with GPT-4o generated ones. \n\n### 5. Synergy Mechanism - Attention Score\nWe further prove the object-part synergy mechanism by reporting the **average attention score**, since the attention maps cannot be provided in the rebuttal format. The average attention score is calculated by summing attention scores of true positive predictions inside the ground truth masks, divided by the area of the masks. The attention is the normalized cos similarity between object queries and the dense features of the final layer of the object/part decoder. The score shows the amount of attention correctly assigned by the model to the ground truth area, and is mapped to the range [0, 1].\n\nIn the setting of PPS116+INS+PART -> PartImageNet, as shown in Table R-11, compared to the \"detached object-part seg.\", the synergized object-part segmentation leads to higher attention scores for both object and part segmentation, proving strong evidence of the synergy between both segmentation tasks. \n| Setting | Detached Obj-Part Seg|Obj-Part Seg in Synergy   |\n|-------------|--------|--------|\n| obj attn. score    | 0.76 | 0.82 |\n| part attn. score    | 0.58 | 0.67 |\n\n**Table R-11:** Attention score of the object and part segmentation.\n\nWe will further show the attention maps and the attention scores in more experimental settings in the revision.\n\n### 6. Comparison to PartCatSeg\nWe agree that the improvement over PartCatSeg in OVPS is moderate in a specific dataset (ADE20K).\nHowever, it is noticeable that our method is developed and trained with object-part-level instance segmentation. Directly evaluating our method in the semantic segmentation (Table 3 in the main paper) still leads to superior performance (hIOU) in PPS-116 and PartImageNet datasets, showing the great potential of the proposed method."}, {"review_id": "vWZ5jMIou6", "reviewer": "Reviewer_rNHe", "summary": "* This paper presents LangHOPS, a new framework for Open-Vocabulary Part Instance Segmentation (OVPIS).\n* LangHOPS explicitly embeds object–part hierarchies in language space, providing a structured representation of object-part relationships.\n* The method integrates a Multimodal Large Language Model (MLLM) to enable context-aware and granularity-adaptive object-part parsing.\n* Compared to previous works based on heuristics or constrained CLIP embedding space reasoning, LangHOPS introduces language-grounded part queries for accurate and scalable instance-level segmentation.", "questions": "* In Table 1 and Table 2, experiments are conducted using Pascal-Part-116, which, to my understanding, only provides semantic part segmentation annotations (e.g., \"bus's headlight\"). Could the authors clarify how part instance segmentation (e.g., \"bus 1's headlight 1\") is performed using this dataset? Specifically, how was the OVPIS task implemented on PPS-116, which lacks instance-level part annotations?\n* In Figure 1, the second example (two people walking) appears to demonstrate semantic segmentation, rather than instance-level segmentation, particularly for the parts. Could the authors clarify whether this example illustrates part instance segmentation or standard semantic segmentation?\n* In Line 192, the paper provides an example of the structured prompt input for the MLLM-based object-part parsing. Could the authors elaborate on what specific outputs are produced by the MLLM at this stage?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n* The paper introduces the first integration of Multimodal Large Language Models (MLLMs) for the task of object-part instance segmentation, demonstrating originality.\n* By explicitly modeling language-grounded object–part hierarchies, LangHOPS improves part query representation, enabling enhanced multi-granularity reasoning and context awareness compared to prior CLIP-based methods.\n* The experimental evaluation is covering major benchmarks, including PartImageNet, Pascal-Part-116, and ADE20K, and demonstrates scalability, with further performance gains observed by incorporating additional training data.\n\n### Weaknesses\n* The proposed approach appears to be an extension of PartGLEE with an integrated MLLM, but the architectural or training-level novelties beyond this integration are not clearly articulated. A more precise explanation of additional contributions, if any, would strengthen the claim of originality.\n* Since LangHOPS relies on an MLLM (specifically, PaliGemma2), it would strengthen the work to provide quantitative comparisons regarding computational resources, such as model parameters and inference costs, relative to baseline methods.\n* In Section 3.2 Method Overview, the sub-section title \"Object Segmentation\" is redundantly repeated in Lines 130 and 133, which should be clarified. Furthermore, inconsistencies in terminology between Figure 2, Section 3.2, and subsequent subsections (e.g., Object-Part Parsing, Part Segmentation) could lead to confusion and should be aligned.\n* As noted in the NeurIPS Paper Checklist [7. Experiment statistical significance], the experimental results lack information on error bars or measures of variability, limiting the assessment of result consistency and robustness.\n* The NeurIPS Paper Checklist indicates \"[5. Open access to data and code] Yes,\" but I could not find a clear link or reference for accessing the released data and code at the time of reviewing.\n* Minor typos present in the manuscript:\n\t* Line 50: produing → producing\n\t* Line 131: featrues → features\n\t* Line 243: dateset → dataset\n\t* Table 1: artImageNet → PartImageNet", "was_revised": true, "final_justification": "In my original review, I rated the paper as a borderline reject (score 3) due to concerns about clarity, architectural novelty beyond MLLM integration, and presentation inconsistencies.  \nThe rebuttal and subsequent revisions have directly addressed these points.  \nThe authors clarified how their language-grounded object–part hierarchy differs fundamentally from prior approaches, provided instance-specific representation examples, and reorganized the method section for clearer structure and terminology.  \nThey also added statistical robustness analyses, clarified computational costs, and fixed typographical and notation issues.  \nThe method remains the first to integrate an MLLM into OVPIS with consistent SOTA results across benchmarks, and the strengthened presentation makes the contribution clearer and more convincing.  \nWhile some improvements could still be made in presentation polish, the technical merit and demonstrated effectiveness now outweigh earlier reservations.  \nI am therefore raising my rating from weak reject (3) to 4 (weak accept).", "rebuttal": "We appreciate the reviewer highlighting the originality of our work by introducing language-grounded object-part hierarchies and MLLMs into the object-part instance segmentation task, as well as, in line with other reviewers, our experiments verifying our approach and showing the superior performance.\n\n### 1. Novelty Clarification\nOur work is not an extension of PartGlee with the integration of an MLLM, but rather proposes novel intermediate representations to enable language-grounded reasoning and the corresponding training strategy. In detail, our additional contributions are as follows:\n\n- Our method introduces **language-grounded modeling** throughout the whole architecture, closely coupling object and part decoding as well as query generation in the same space. Furthermore, the language-grounded object-part hierarchy modeling replaces the brute-force hierarchical representation using a fixed set of parts in the Q-Former of PartGLEE's architecture. Overall, this design enables adaptive part hierarchies in our framework and OVS adaptation to varying part granularity beyond the training data.\n- We explore the recipes of both **one-stage and two-stage training strategies**. We provide an empirical study (Table 7 in the submission) showing that the one-stage training strategy leads to better in-domain performance while the two-stage one leads to better cross-dataset performance. This empirical finding suggests that decoupling coarse-to-fine supervision helps improve generalization in tasks involving hierarchical modeling, offering practical insights for similar structured perception problems.\n- **Architectural Verification:** We conducted experiments to verify and derive our architecture against a direct extension of PartGLEE, PSALM, and PartCatSeg. Most importantly, our experiments (Table 4 main paper) show that our design improves performance far beyond only utilizing the additional capacity and pretraining of the MLLM, but rather builds a framework around it to effectively utilize its generalist capabilities. We add these verification experiments to the final version of our paper.\n\nTogether, these contributions show that our method is not merely an MLLM extension of PartGLEE, but introduces important innovations in representation design and training methodology that contribute to its effectiveness.\n\n### 2. Parameters and Inference Cost\nWe now report the footprint of GPU hours, carbon cost, inference cost, and model size of PartGLEE, PSAL,M and LangHOPS in Table R-9. The GPU hours and inference time are reported for a NVIDIA H200 GPU. The spec. power (700W) of H200 and world average carbon intensity of electricity (~0.475 kg CO₂e / kWh) are used for calculating the footprint.\n\n| Method | Model Size | Training GPU Hours  | Training Footprint (kg CO₂e) | Inference Time (ms) |\n|-|-|-|-|-|\n| PSALM†| 1.5B|  92| 30.6 | 628  |\n| PartGLEE | 1B | 40| 13.3 | 240 |\n| LangHOPS | 4B | 72 | 23.9 | 396|\n\n**Table R-9:** Specs of LangHOPS and the baselines. PPS116 + INS + PART -> PartImageNet.\n\nThe Table shows that LangHOPS has the largest model size, mainly due to the usage of MLLM (Paligemma2-3B). PSALM† has the longest training time and carbon footprint since it trains the LLM instead of using LoRA, and needs to process all candidate category names, which leads to long input prompts to the LLM. LangHOPS achieves the best performance with reasonable training and inference cost compared to the baselines.\n\n### 3. Terminology Inconsistency\nWe appreciate the reviewer's correction. The second \"Object Segmentation\" in L133 is indeed redundant, and that paragraph is part of object-part parsing. We will address them in the revised version of the paper. \"Language-Space Object-Part Representation\" and \"MLLM-based Object-Part Parsing\" are actually both part of \"Object-Part Parsing\" in Figure 2. We understand that it can cause confusion, and we will clarify this. \n\n### 4. Error Bars\nWe have now included mean ± standard deviation results computed over three random seeds (20, 43, and 4337) for both LangHOPS and PartGlee, the strongest baseline. \n\nThese results are reported in Tables R-5 and R-6. As shown, the averaged AP values closely align with the originally reported numbers, slightly increasing in most experiments for our method. The standard deviations have a low magnitude, indicating that our results are stable and show a significant improvement over the PartGlee baseline. \n\n| Method|PPS-116|-|-| +INS | -| - | +INS+PART |-| -| PartImageNet|-| -|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|| obj  | part | AP| obj  | part | AP | obj| part | AP | obj  | part | AP|\n| PartGlee ± | 38.4±0.5 | 8.61±0.46 | 15.2±0.5 | 57.6±1.8 | 11.3±0.2 | 21.5±0.6 | 60.1±0.9 | 10.5±0.7 | 21.5±0.7 | 82.0±0.5 | 42.5±0.8 | 51.3±0.7 |\n| LangHOPS±| 48.7±3.3|8.89±0.24 | 17.7±0.9 | 60.9±0.7|12.1±1.0|22.9±1.0 | 63.7±1.4 | 16.6±0.3 | 27.0±0.5 | 84.1±1.0 |48.2±0.8| 56.1±0.8 |\n\n**Table R-5:** PPS116→PartImageNet with standard deviation (±) of the proposed method LangHOPS± and the strongest baseline PartGlee±.\n\n| Method|PartImageNet|-|-|+INS| -|-|+INS+PART |-| -|PPS-116|-| -|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| | obj | part | AP  | obj |part | AP| obj | part | AP| obj | part | AP  |\n| PartGlee ± | 8.53±0.52 | 2.05±0.09 | 3.04±0.16 | 23.3±0.1 | 3.10±0.16 | 6.17±0.16 | 22.5±0.4 | 3.60±0.24 | 6.46±0.26 | 53.6±1.5 | 13.8±0.6 | 19.9±0.7 |\n| LangHOPS ±| 11.0±1.0 | 2.17±0.03 | 3.50±0.18 | 22.9±0.7 | 3.68±0.11 | 6.59±0.19 | 23.2±0.5 | 4.51±0.25 | 7.34±0.28| 55.0±1.1 | 15.0±0.2 | 21.1±0.3 |\n\n**Table R-6:** PartImageNet→PPS116 with standard deviation..\n\n### 5. Code Release\nWe will make the code public upon the paper’s release. Due to the IP limitations of the work, it was not possible to release the code during review.\n\n### 6. Typos\nWe thank the reviewer for reporting the typos. We will address them and go through the paper carefully, correcting them.\n\n### 7. Instance-level Annotation\nThe reviewer is correct that the original Pascal-Part-116 [42] dataset only provides semantic part segmentation annotations. However, as detailed in Appendix B of PartGLEE [20] and its official git repository (assets/DATA.md), PartGLEE further processes these annotations to produce instance-level part segmentation labels, which support the OVPIS task. Our experiments in Table 1 and Table 2 use this PartGLEE-provided version of the PPS-116 dataset, which includes part-instance-level annotations. We will clarify this in the revised version of the paper to avoid confusion.\n\n### 8. Visualization Clarification\nThe visualizations in Figure 1 are part instance segmentation outputs. For readability, we display each part belonging to the same type in the same color, allowing the reader to reidentify objects and show only a single label for each class of parts to avoid clutter. We thank the reviewer for pointing out this potentially distracting visualization and will provide a more common visualization showing the separation of instances in the final paper.\n\n### 9. MLLM Output\nThe MLLM outputs are embeddings of MLLM-processed prompt tokens, and we extract part queries from those embeddings. Specifically, we feed a structured language prompt—containing object-part hierarchical information—into the MLLM. Unlike typical generative usage, the MLLM here returns token-level embeddings for the entire input sequence, with the output length matching the input. We then extract the embeddings at the positions corresponding to the initial part query tokens. These embeddings are used as the refined, language-informed part queries, which are passed to the visual part decoder."}, {"review_id": "M3nGVZjGkv", "reviewer": "Reviewer_Ku9B", "summary": "The authors propose a new method for Open-vocabulary Part Instance Segmentation (OVPIS) called LangHOPS by using hierarchical modeling in language space to encode of object parts. LangHOPS performs better than existing state-of-the-art methods across multiple benchmarks.", "questions": "Please see weaknesses", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n\n1. LangHOPS integrates MLLMs for OVPIS and grounds object-part hierarchies in language space. This is novel and superior to prior methods that struggle with modeling hierarchical relationships or handling part granularity variations.\n2. The method achieves strong empirical results over baselines.\n\nWeaknesses:\n\n1. The object part granularity can be at different levels (as also seen from the supplementary figure 4). How to account for such ambiguities during evaluation? For example, if face is a sub-part for a human, what happens if the module parts with higher granularity like eyes, nose, mouth, etc?\n2. The paper's math notations use incorrect formatting throughout. For instance, $C_{obj}$ should be $C_\\text{obj}$ and $C_{part}$ should be $C_\\text{part}$ and so forth. This inconsistency hampers readability, and I strongly encourage the authors to correct these notation errors.", "was_revised": true, "final_justification": "I thank the authors for their response. The authors have sufficiently addressed my concerns, hence I maintain my positive rating.", "rebuttal": "We appreciate the reviewer’s positive feedback on our work, especially the highlighting of novelty by integrating MLLMs deeply into the OVPIS task and the handling of varying part granularity based on this. Furthermore, our core contribution grounding object-part hierarchies in the language space and performance over prior baselines is well acknowledged.\n\nWe address the reviewer's concerns in the following sections.\n### 1. Granularity Variation\nPart granularity is guided by text and can be flexibly specified by the user or by the dataset annotations used for training and evaluation in our approach.\n- For example, during evaluation, if the ground truth annotation specifies “face” as the part of interest for a “human” object, our model constructs the corresponding part query by concatenating the text embedding of “face” with that of “human”, resulting in a semantically aligned object-part query (e.g., “human-face”).\n- This design allows the model to be explicitly guided to the appropriate level of granularity, depending on the dataset’s supervision or user input. Moreover, our cross-dataset evaluation (Tables 1 and 2 main paper) is specifically intended to test this capability: different datasets may annotate parts at different levels of detail, and our model’s language-grounded hierarchy allows it to adapt accordingly.\n- This granularity awareness is a key reason why our method consistently outperforms baseline models in the cross-dataset setting. \n\nWe will include more visualization of the model’s predictions on the same image but under different specified part granularities to better illustrate the model’s flexibility in handling varying granularity levels.\n\n### 2. Notation\nWe appreciate the reviewer’s feedback and agree that the mathematical notations should be clear and consistent. We have carefully revised all equations and these corrections will be reflected in the updated version."}]

Author rebuttal:

Meta-review: The paper ultimately received three positive reviews (two Borderline Accepts and one Accept) and a single negative review (Borderline Reject).

The positive reviewers found their concerns are fully addressed during the post-rebuttal discussion, leading two of them to raise their scores. While one reviewer remained explicitly negative, the authors provided thorough and convincing responses that the other reviewers found to sufficiently address concerns regarding novelty, motivation, and technical contributions.

Overall, the AC believes the merits of the paper outweigh its flaws and is therefore recommending acceptance.
Decision: Accept (poster)