Title: Hierarchical Open-vocabulary Universal Image Segmentation
Venue: NeurIPS 2023
Accepted: yes
Keywords: Universal Image Segmentation

Hierarchical

Open-vocabulary
Abstract: Open-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions. However, complex visual scenes can be naturally decomposed into simpler parts and abstracted at multiple lev4 els of granularity, introducing inherent segmentation ambiguity. Unlike existing methods that typically sidestep this ambiguity and treat it as an external factor, our approach actively incorporates a hierarchical representation encompassing different semantic-levels into the learning process. We propose a decoupled text-image fusion mechanism and representation learning modules for both “things” and “stuff”. Additionally, we systematically examine the differences that exist in the textual and visual features between these types of categories. Our resulting model, named HIPIE, tackles HIerarchical, oPen-vocabulary, and unIvErsal segmentation tasks within a unified framework. Benchmarked on diverse datasets, e.g., ADE20K,COCO, Pascal-VOC Part, and RefCOCO/RefCOCOg, HIPIE achieves the state-of14 the-art results at various levels of image comprehension, including semantic-level (e.g., semantic segmentation), instance-level (e.g., panoptic/referring segmentationand object detection), as well as part-level (e.g., part/subpart segmentation) tasks.

Hierarchical Open-vocabulary Universal Image Segmentation

Abstract

Open-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions. However, complex visual scenes can be naturally decomposed into simpler parts and abstracted at multiple levels of granularity, introducing inherent segmentation ambiguity. Unlike existing methods that typically sidestep this ambiguity and treat it as an external factor, our approach actively incorporates a hierarchical representation encompassing different semantic-levels into the learning process. We also propose a decoupled text-image fusion mechanism and representation learning modules for both ªthingsº and ªstuffº. 1 Additionally, we systematically examine the differences that exist in the textual and visual features between these types of categories. Our resulting model, named HIPIE , tackles HI erarchical, o P en-vocabulary, and un I v E rsal segmentation tasks within a unified framework. Benchmarked on over 40 datasets, e.g. , ADE20K, COCO, Pascal-VOC Part, RefCOCO/RefCOCOg, ODinW and SeginW, HIPIE achieves the state-of-the-art results at various levels of image comprehension, including semantic-level ( e.g. , semantic segmentation), instance-level ( e.g. , panoptic/referring segmentation and object detection), as well as part-level ( e.g. , part/subpart segmentation) tasks.

1 Introduction

Image segmentation is a fundamental task in computer vision, enabling a wide range of applications such as object recognition, scene understanding, and image manipulation [50, 14, 42, 7, 37]. Recent advancements in large language models pave the way for open-vocabulary image segmentation, where models can handle a wide variety of object classes using text prompts. However, there is no single ªcorrectº way to segment an image. The inherent ambiguity in segmentation stems from the fact that the interpretations of boundaries and regions within an image depend on the specific tasks.

Existing methods for open-vocabulary image segmentation typically address the ambiguity in image segmentation by considering it as an external factor beyond the modeling process. In contrast, we adopt a different approach by embracing this ambiguity and present HIPIE , as illustrated in Fig. 1, a novel HI erarchical, o P en-vocabulary and un I v E rsal image segmentation and detection model. This includes semantic-level segmentation, which focuses on segmenting objects based on their semantic meaning, as well as instance-level segmentation, which involves segmenting individual instances of objects or groups of objects ( e.g. , instance and referring segmentation).

Image

HIPIE

Panoptic Segment & Detection:

'person, sky, tree, building …'

Part Segment:

'person head, leg, arm …'

Referring Segment:

'beach umbrella with white pole'

Subpart Segment:

'person eye, mouth, cat neck…'

…

Model Inference

Whole

Part

Subpart

Referring Segmentation

Person

Cat

Head

Torso

Eye

Ear

Hair

Neck

…

…

…

Hierarchy

Raw Image

SAM

Grounded SAM

HIPIE + SAM

Additionally, our model captures finer details by incorporating part-level segmentation, which involves segmenting object parts/subparts. By encompassing different granularity, HIPIE allows for a more comprehensive and nuanced analysis of images, enabling a richer understanding of their contents.

To design HIPIE, we begin by investigating the design choices for open-vocabulary image segmentation (OIS). Existing methods on OIS typically adopt a text-image fusion mechanism, and employ a shared representation learning module for both stuff and thing classes [4, 62, 58, 10, 56]. Fig. 2 shows the similarity matrics of visual and textual features between stuff and thing classes. On this basis, we can derive several conclusions:

Noticeable discrepancies exist in the betweenclass similarities of textual and visual features between stuff and thing classes.

Stuff classes exhibit significantly higher levels of similarity in text features than things.

Visual Features (MAE)

Textual Features (BERT)

0.5

1.0

Pretrain

Ours

thing

stuff

thing

stuff

thing

stuff

thing

stuff

0.0

0.5

μ

= 0.88

μ

= 0.92

μ

= 0.32

μ

= 0.40

20

02

40

60

100

100

120

120

301

120

20

00

001

120

20

20

40

10

50

50

DE

OE

00t

100

T

120

120

60

80

100

120

60

80

100

120

Open

Vocab.

Instance

Segment.

Semantic

Segment.

Panoptic

Segment.

Referring

Segment.

Cls-agnostic

Part Seg.

Cls-aware

Part Seg.

Object

Detection

SAM [24]

✗

✓

✗

✗

✗

✓

✗

*

SEEM [67]

✓

✓

✓

✓

✓

✗

✗

*

ODISE [56]

✓

✓

✓

✓

✗

✗

✗

*

UNINEXT [58]

²

✓

✗

✗

✓

✗

✗

✓

X-Decoder [66]

✓

✓

✓

✓

✓

✗

✗

*

G-DINO [36]

✓

✓

✗

✗

✓

✗

✗

✓

PPS [5]

✗

✗

✗

✗

✗

✓

✓

✗

HIPIE

✓

✓

✓

✓

✓

✓

✓

✓

vs. prev. SOTA

-

+5.1

+2.0

+1.3

+0.5

-

+5.2

+3.2

This observation suggests that integrating textual features may yield more significant benefits in generating discriminative features for thing classes compared to stuff classes. Consequently, for thing classes, we adopt an early image-text fusion approach to fully leverage the benefits of discriminative textual features. Conversely, for stuff classes, we utilize a late image-text fusion strategy to mitigate the potential negative effects introduced by non-discriminative textual features. Furthermore, the presence of discrepancies in the visual and textual features between stuff and thing classes, along with the inherent differences in their characteristics (stuff classes requiring better capture of texture and materials, while thing classes often having well-defined geometry and requiring better capture of shape information), indicates the need for decoupling the representation learning modules for producing masks for stuffs and things.

In addition to instance/semantic-level segmentation, our model is capable of open-vocabulary hierarchical segmentation. Instead of treating part classes, like 'dog leg', as standard multi-word labels, we concatenate class names from different granularity as prompts. During training, we supervise the classification head using both part labels, such as 'tail', and instance labels, such as 'dog', and we explicitly contrast a mask embedding with both instance-level and part-level labels. In the inference stage, we perform two separate forward passes using the same image but different prompts to generate instance and part segmentation. This design choice empowers open-vocabulary hierarchical segmentation, allowing us to perform part segmentation on novel part classes by randomly combining classes from various granularity, such as 'giraffe' and 'leg', even if they have never been seen during training. By eliminating the constraints of predefined object classes and granularity, HIPIE offers a more flexible and adaptable solution for image segmentation.

We extensively benchmark HIPIE on various popular datasets to validate its effectiveness, including MSCOCO, ADE20K, Pascal Panoptic Part, and RefCOCO/RefCOCOg. HIPIE achieves state-of-theart performance across all these datasets that cover a variety of tasks and granularity.

To the best of our knowledge, HIPIE is the first hierarchical, open-vocabulary and universal image segmentation and detection model (see Table 1). By decoupling representation learning and textimage fusion mechanisms for things vs. stuff classes, HIPIE overcomes the limitations of existing approaches and achieves state-of-the-art performance on various benchmarks.

2 Related Works

Open-Vocabulary Semantic Segmentation [2, 53, 26, 16, 44, 32, 54, 55] aims to segment an image into semantic regions indicated by text descriptions that may not have been seen during training. ZS3Net [2] combines a deep visual segmentation model with an approach to generate

visual representations from semantic word embeddings to learn pixel-wise classifiers for novel categories. LSeg [26] uses CLIP's text encoder [43] to generate the corresponding semantic class's text embedding, which it then aligns with the pixel embeddings. OpenSeg [16] adopts a grouping strategy for pixels prior to learning visual-semantic alignments. By aligning each word in a caption to one or a few predicted masks, it can scale-up the dataset and vocabulary sizes. GroupViT [54] is trained on a large-scale image-text dataset using contrastive losses. With text supervision alone, the model learns to group semantic regions together. OVSegmentor [55] uses learnable group tokens to cluster image pixels, aligning them with the corresponding caption embeddings.

Open-Vocabulary Panoptic Segmentation (OPS) unifies semantic and instance segmentation, and aims to perform these two tasks for arbitrary categories of text-based descriptions during inference time [10, 56, 66, 67, 58]. MaskCLIP [10] first predicts class-agnostic masks using a mask proposal network. Then, it refines the mask features through Relative Mask Attention interactions with the CLIP visual model and integrates the CLIP text embeddings for open-vocabulary classification. ODISE [56] unifies Stable Diffusion [47], a pre-trained text-image diffusion model, with text-image discriminative models, e.g. CLIP [43], to perform open-vocabulary panoptic segmentation. XDecoder [66] takes two types of queries as input: generic non-semantic queries that aim to decode segmentation masks for universal segmentation, and textual queries to make the decoder languageaware for various open-vocabulary vision tasks. UNINEXT [58] unifies diverse instance perception tasks into an object discovery and retrieval paradigm, enabling flexible perception of open-vocabulary objects by changing the input prompts.

Referring Segmentation learns valid multimodal features between visual and linguistic modalities to segment the target object described by a given natural language expression [19, 60, 20, 22, 13, 59, 52, 35, 63]. It can be divided into two main categories: 1) Decoder-fusion based method [8, 51, 63, 35] first extracts vision features and language features, respectively, and then fuses them with a multimodal design. 2) Encoder-fusion based method [13, 59, 30] fuses the language features into the vision features early in the vision encoder.

Parts Segmentation learns to segment instances into more fine-grained masks. PPP [5] established a baseline of hierarchical understanding of images by combining a scene-level panoptic segmentation model and part-level segmentation model. JPPF [21] improved this baseline by introducing joint Panoptic-Part Fusion module that achieves comparable performance with significantly smaller models.

Promptable Segmentation. The Segment Anything Model (SAM) [24] is an approach for building a fully automatic promptable image segmentation model that can incorporate various types of human interventions, such as texts, masks, and points. SEEM [67] proposes a unified prompting scheme that encodes user intents into prompts in a joint visual-semantic space. This approach enables SEEM to generalize to unseen prompts for segmentation, achieving open-vocabulary and zero-shot capabilities. Referring segmentation can also be considered as promptable segmentation with text prompts.

Comparison with Previous Work. Table 1 compares our HIPIE method with previous work in terms of key properties. Notably, HIPIE is the only method that supports open-vocabulary universal image segmentation and detection, enabling the object detection, instance-, semantic-, panoptic-, hierarchical-(whole instance, part, subpart), and referring-segmentation tasks, all within a single unified framework.

3 Method

We consider all relevant tasks under the unified framework of language-guided segmentation, which performs open-vocabulary segmentation and detection tasks for arbitrary text-based descriptions.

3.1 Overall Framework

The proposed HIPIE model comprises three main components, as illustrated in Fig. 3:

1) Text-image feature extraction and information fusion (detailed in Secs. 3.2 to 3.4): We first generate a text prompt T from labels or referring expressions. Then, we extract image ( I ) and text ( T ) features F v = Enc v ( I ) , F t = Enc t ( T ) using image encoder Enc v and text encoder Enc t , respectively. We then perform feature fusion and obtain fused features F ′ v , F ′ t = FeatureFusion ( F v , F t ) .

Image

Encoder

Text

Encoder

thing queries

Thing

Decoder

Stuff

Decoder

stuff queries

Text-Image

Fusion

F v

F t

F

′

t

F

′

v

Image

Text Prompt

Panoptic Segment:

'person, sky, tree, building

…'

Referring Segment:

'Girl covering her face'

Part Segment:

'person head, leg, arm …'

Subpart Segment:

'person eye, cat neck, ear…'

…

2) Foreground (referred to as things) and background (referred to as stuffs) mask generation (detailed in Sec. 3.5): Each of the decoders takes in a set of image features and text features and returns sets of masks, bounding boxes, and object embeddings ( M,B,E ) . We compute the foreground and background proposals and concatenate them to obtain the final proposals and masks as follows:

where ⊕ denotes the concatenation operation.

3) Proposal and mask retrieval using text prompts (detailed in Sec. 3.6): To assign class labels to these proposals, we compute the cosine similarity between object embedding E and the corresponding embedding E ′ i of class i ∈ { 1 , 2 ..., c } . For a set of category names, the expression is a concatenated string containing all categories. We obtain E ′ i by pooling tokens corresponding to each label from the encoded sequence F t . For referring expressions, we taken the [CLS] token from BERT output as E ′ i .

3.2 Text Prompts

Text prompting is a common approach used in open-vocabulary segmentation models [19, 60, 57, 58].

For open-vocabualry instance segmentation, panoptic segmentation, and semantic segmentation, the set of all labels C is concatenated into a single text prompt T i using a ª.º delimiter. Given an image I and a set of text prompts T , the model aims to classify N masks in the label space C ∪ { ' other ' } , where N is the maximum number of mask proposals generated by the model.

For referring expressions, the text prompt is simply the sentence itself. The goal is to locate one mask in the image corresponding to the language expression.

3.3 Image and Text Feature Extraction

We employ a pretrained BERT model [6] to extract features for text prompts. Because the BERT-base model can only process input sequences up to 512 tokens, we divide longer sequences into segments of 512 tokens and encode each segment individually. The resulting features are then concatenated to obtain features of the original sequence length.

Weutilize ResNet-50 [18] and Vision Transformer (ViT) [11] as base architectures for image encoding. In the case of ResNet-50, we extract multiscale features from the last three blocks and denote them as F v . For ViT, we use the output features from blocks 8, 16, and 32 as the multiscale features F v .

stuff queries

Stuff

Decoder

Thing

Decoder

thing queries

Boxes

& Masks

Text-

Image

Fusion

Thing & Stuff

Decoder

thing & stuff queries

Boxes

& Masks

Text-

Image

Fusion

text

feature

Image

feature

text

feature

Image

feature

stuff queries

Stuff

Decoder

Thing

Decoder

thing queries

Boxes

& Masks

Text-

Image

Fusion

text

feature

Image

feature

a)

b)

c)

3.4 Text-Image Feature Fusion

We explored several design choices for text-image feature fusion and mask generation modules as shown in Fig. 4 and Table 5, and discovered that Fig. 4c) can give us the optimal performance. We adopt bi-directional cross-attention ( Bi -Xattn ) to extract text-guided visual features F t 2 v and image-guided text features F v 2 t . These attentive features are then integrated with the vanilla text features F t and image features F v through residual connections, as shown below:

where F v and F t represent the visual and text-prompt features, respectively.

3.5 Thing and Stuff Mask Generation

We then generate masks and proposals for the thing and stuff classes by utilizing F ′ v and F ′ t that we obtained in Sec. 3.4.

Model Architecture. While architectures such as Mask2Former and MaskDINO [4, 28] can perform instance, semantic and panoptic segmentation simultaneously, models trained jointly show inferior performance compared with the same model trained for a specific task ( e.g. instance segmentation only). We hypothesize that this may result from the different distribution of spatial location and geometry of foreground instance masks and background semantic masks. For example, instance masks are more likely to be connected, convex shapes constrained by a bounding box, whereas semantic masks may be disjoint, irregular shapes spanning across the whole image.

To address this issue, in a stark contrast to previous approaches [58, 36, 57] that use a unified decoder all both stuffs and things, we decouple the stuff and thing mask prediction using two separate decoders. For the thing decoder, we adopt Deformable DETR [65] with a mask head following the UNINEXT [58] architecture and incorporate denoising procedures proposed by DINO [62]. For the stuff decoder, we use the architecture of MaskDINO [28].

Proposal and Ground-Truth Matching Mechanisms. We make the following distinctions between the two heads. For thing decoder, we adopt simOTA [15] to perform many-to-one matching between box proposals and ground truth when calculating the loss. We also use box-iou-based NMS to remove duplicate predictions. For the stuff decoder, we adopt one-to-one Hungarian matching [25]. Additionally, we disable the box loss for stuff masks. We set the number of queries to 900 for the things and 300 for the stuffs.

Loss Functions. For both decoders, we calculate the class logits as the normalized dot product between mask embeddings ( M ) and text embeddings ( F ′ t ). Weadopt Focal Loss [33] for classification outputs, L1 loss, and GIoU loss [45] for box predictions, pixel-wise binary classification loss and DICE loss [49] for mask predictions. Given predictions ( M 1 , B 1 , E 1 ) , ( M 2 , B 2 , E 2 ) , groundtruth labels ( M ′ , B ′ , C ) and its foreground and background subset ( M ′ f , B ′ f , C f ) and ( M ′ b , B ′ b , C b ) , The final Loss is computed as

where L box = λ L 1 L L 1 + λ giou L giou , L mask = λ ce L ce + λ dice L dice, and L cls = L focal. Note that while we do not use the stuff decoder for thing prediction, we still match its predictions with things and

person

head

torso

upper arm

hair

face

eye

nose

mouth

eyebrow

ear

HIPIE

T1

T2

T3

… TN

head

eye

nose

…

tail

T1

T2

T3

… TN

person

cat

wall

…

ceiling

compute the class and box losses in the training. We find such auxiliary loss setup make the stuff decoder aware of the thing distribution and imporves the final performance.

3.6 Open-Vocabulary Universal Segmentation

In closed set setting, we simply merge the output of two decoders and perform the standard postprocessing of UNINEXT [58] and MaskDINO [28] to obtain the final output.

In zero-shot open vocabulary setting, we follow ODISE [56] and combining our classification logits with a text-image discriminative model, e.g. , CLIP [43]. Specially, given the a mask M on image I , its features E and test classes C test, we first compute the probability p 1 ( E,C test ) = P ( C test | E ) in the standard way as mentioned before. We additionally compute mask-pooled features of M from the vision encoder V of CLIP as E CLIP = MaskPooling ( M, V ( I )) . Then we compute the CLIP logits p 2 ( E,C test ) = P ( C test | E CLIP ) as the similarity between the CLIP text features and the E CLIP. Finally we combine the final prediction as

Where λ is a balancing factor. Emprically, we found such setting leads to better performance than naively relying completely on CLIP features only or close-set logits.

3.7 Hierarchical segmentation

In addition to the instance-level segmentation, we can also perform part-aware hierarchical segmentation. We concatenate the instance class names and part class names as labels. Some examples are "human ear", and "cat head". In the training process, we supervise the classification head with both part labels and instance labels. Specifically, we replace L cls with L clsPart + L clsThing in Eq. (3). We combine parts segmentation and instance segmentation of the same image to get part-aware instance segmentation. Additionally, layers of hierarchy is obtained by grouping the parts. For example, the "head" consists of ears, hair, eyes, nose, etc. Fig. 5 illustrates this process. Fig. A1 highlights the difference of our approach with other methods.

3.8 Class-aware part segmentation with SAM

We can also perform the class-aware hierarchical segmentation by combining our semantic output with class-agnostic masks produced by SAM [24]. Specifically, given semantic masks M , their class probability P M , and SAM-generated part masks S , we compute the class probability of mask S i ∈ S with respect to class j as

Where | M k ∩ S i | is the area of intersection between mask M k and S i . We combine our semantic output with SAM because our pretraining datasets only contains object-centric masks, whereas the SA-1B dataset used by SAM contains many local segments and object parts.

HIPIE

UNINEXT

HIPIE

UNINEXT

Known

Novel

Hierarchy

Hierarchy

Dog

--leg

Giraffe

--tail

--head

--torso

IEI

4 Experiments

We comprehensively evaluate HIPIE through quantitative and qualitative analyses to demonstrate its effectiveness in performing various types of open-vocabulary segmentation and detection tasks. The implementation details of HIPIE are explained in Sec. 4.1. Sec. 4.2 presents the evaluation results of HIPIE. Additionally, we conduct an ablation study of various design choices in Sec. 4.3.

4.1 Implementation Details

Evaluation Metrics. Semantic Segmentation performance is evaluated using the mean IntersectionOver-Union (mIoU) metric. For Part segmentation, we report mIoUPartS, which is the mean IoU for part segmentation on grouped part classes [5]. Object Detection and Instance Segmentation results are measured using the COCO-style evaluation metric - mean average precision (AP) [34]. Panoptic Segmentation is evaluated using the Panoptic Quality (PQ) metric [23]. Referring Image Segmentation (RIS) [19, 60] is evaluated with overall IoU (oIoU).

4.2 Results

Panoptic Segmentation. We examine Panoptic Quality (PQ) performance across MSCOCO [34] for closed-set and ADE20K [64] for open-set zero shot transfer learning. Based on Table 3 our model is able to outperform the previous close-set state-of-the-art using a ViT-H backbone by +1.8. In addition, we match the best open-set PQ results, while being able to run on more tasks and having a simpler backbone than ODISE [56]. Semantic Segmentation. The evaluation of our model's performance on various open-vocabulary semantic segmentation datasets is presented in Table 4. These datasets include: 1) A-150: This dataset comprises 150 common classes from ADE20K [64]. 2) A-847: This dataset includes all 847 classes from ADE20K [64]. 3) PC-59: It consists of 59 common classes from Pascal Context [39]. 4) PC-459: This dataset encompasses the full 459 classes of Pascal Context [39]. 5) PAS-21: The vanilla Pascal VOC dataset [12], containing 20 foreground classes and 1 background class. These diverse datasets enable a comprehensive evaluation of our model's performance across different settings, such as varying class sizes and dataset complexities. Table 4 provides insights into how our model performs in handling open-vocabulary semantic segmentation tasks, demonstrating

its effectiveness and versatility in detecting and segmenting a wide range of object categories in real-world scenarios.

Part Segmentation. We evaluate our models performance on Pascal-Panoptic-Parts dataset [5] and report mIoUpartS in Table 3. We followed the standard grouping from [5]. Our model outperforms state-of-the-art by +5.2 in this metric. We also provide qualitative comparisons with Grounding DINO + SAM in Fig. 7. Our findings reveal that the results of Grounding SAM are heavily constrained by the detection performance of Grounding DINO. As a result, they are unable to fully leverage the benefits of SAM in producing accurate and fine-grained part segmentation masks.

Raw Image

GDINO+SAM

HIPIE+SAM

Object Detection and Instance Segmentation. We evaluate our model's object detection and instance segmentation capabilities following previous works [28, 67, 56]. On MSCOCO [34] and ADE20K [64] datasets, HIPIE achieves an increase of +5.1 and +0.6 AP mask respectively. Detailed comparisons are provided in Sec. 4.2 which demonstrate state-of-the-art results on ResNet and ViT architectures consistently across all Average Precision metrics.

Referring Segmentation. Referring image segmentation (RIS) tasks are examined using the RefCOCO, RefCOCO+, and RefCOCOg datasets. Our model outperforms all the other alternatives by an average of +0.5 in overall IoU (oIoU).

4.3 Ablation Study

To demonstrate the effectiveness of our design choices for text-image fusion mechanisms and representation learning modules for stuff and thing classes, we conduct an ablation study (depicted in Fig. 4) and present the results in Table 5. From this study, we draw several important conclusions: 1) Text-image fusion plays a critical role in achieving accurate referring segmentation results. 2) The early text-image fusion approach for stuff classes negatively impacts the model's performance on panoptic segmentation. This finding validates our analysis in the introduction section, where we highlighted the challenges introduced by the high levels of confusion in stuff's textual features, which can adversely affect the quality of representation learning. 3) Our design choices significantly improve the performance of panoptic segmentation, instance segmentation, and referring segmentation tasks. These conclusions underscore the importance of our proposed design choices in achieving improved results across multiple segmentation tasks.

5 Conclusions

This paper presents HIPIE, an open-vocabulary, universal, and hierarchical image segmentation model that is capable of performing various detection and segmentation tasks using a unified framework, inculding object detection, instance-, semantic-, panoptic-, hierarchical-(whole instance, part, subpart), and referring-segmentation tasks. Our key insight is that we should decouple the representation learning modules and text-image fusion mechanisms for background ( i.e. , referred to as stuff) and foreground ( i.e. , referred to as things) classes. Extensive experiments demonstrate that HIPIE achieves state-of-the-art performance on diverse datasets, spanning across a wide range of tasks and segmentation granularity.

Acknowledgement Trevor Darrell and XuDong Wang were funded by DoD including DARPA LwLL and the Berkeley AI Research (BAIR) Commons.

References

E. H. Adelson. On seeing stuff: the perception of materials by humans and machines. In Human vision and electronic imaging VI , volume 4299, pages 1±12. SPIE, 2001.

M. Bucher, T.-H. Vu, M. Cord, and P. Pérez. Zero-shot semantic segmentation. In NeurIPS , 2019.

Z. Cai and N. Vasconcelos. Cascade r-cnn: high quality object detection and instance segmentation. IEEE transactions on pattern analysis and machine intelligence , 43(5):1483±1498, 2019.

B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar. Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 1290±1299, 2022.

D. de Geus, P. Meletis, C. Lu, X. Wen, and G. Dubbelman. Part-aware panoptic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5485±5494, 2021.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 , 2018.

N. Dhanachandra, K. Manglem, and Y. J. Chanu. Image segmentation using k-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science , 54:764±771, 2015.

H. Ding, C. Liu, S. Wang, and X. Jiang. Vision-language transformer and query generation for referring segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 16321±16330, 2021.

H. Ding, C. Liu, S. Wang, and X. Jiang. Vlt: Vision-language transformer and query generation for referring segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022.

Z. Ding, J. Wang, and Z. Tu. Open-vocabulary panoptic segmentation with maskclip. arXiv preprint arXiv:2208.08984 , 2022.

A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations , 2020.

M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision , 88:303±338, 2010.

G. Feng, Z. Hu, L. Zhang, and H. Lu. Encoder fusion network with co-attention embedding for referring image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 15506±15515, 2021.

D. A. Forsyth and J. Ponce. Computer vision: a modern approach . prentice hall professional technical reference, 2002.

Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun. Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 , 2021.

G. Ghiasi, X. Gu, Y. Cui, and T.-Y. Lin. Scaling open-vocabulary image segmentation with image-level labels. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXVI , pages 540±557. Springer, 2022.

K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 16000±16009, 2022.

K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 770±778, 2016.

R. Hu, M. Rohrbach, and T. Darrell. Segmentation from natural language expressions. In Proceedings of the European Conference on Computer Vision (ECCV) , 2016.

T. Hui, S. Liu, S. Huang, G. Li, S. Yu, F. Zhang, and J. Han. Linguistic structure guided context modeling for referring image segmentation. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part X 16 , pages 59±75. Springer, 2020.

S. K. Jagadeesh, R. Schuster, and D. Stricker. Multi-task fusion for efficient panoptic-part segmentation. arXiv preprint arXiv:2212.07671 , 2022.

Y. Jing, T. Kong, W. Wang, L. Wang, L. Li, and T. Tan. Locate then segment: A strong pipeline for referring image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 9858±9867, 2021.

A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár. Panoptic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 9404±9413, 2019.

A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al. Segment anything. arXiv preprint arXiv:2304.02643 , 2023.

H. W. Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly , 2(1-2):83±97, 1955.

B. Li, K. Q. Weinberger, S. Belongie, V. Koltun, and R. Ranftl. Language-driven semantic segmentation. In International Conference on Learning Representations , 2022.

F. Li, H. Zhang, S. Liu, J. Guo, L. M. Ni, and L. Zhang. Dn-detr: Accelerate detr training by introducing query denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 13619±13627, 2022.

F. Li, H. Zhang, H. xu, S. Liu, L. Zhang, L. M. Ni, and H.-Y. Shum. Mask dino: Towards a unified transformer-based framework for object detection and segmentation, 2022.

L. H. Li*, P. Zhang*, H. Zhang*, J. Yang, C. Li, Y. Zhong, L. Wang, L. Yuan, L. Zhang, J.-N. Hwang, K.-W. Chang, and J. Gao. Grounded language-image pre-training. In CVPR , 2022.

M. Li and L. Sigal. Referring transformer: A one-step approach to multi-task visual grounding. Advances in neural information processing systems , 34:19652±19664, 2021.

Y. Li, H. Mao, R. Girshick, and K. He. Exploring plain vision transformer backbones for object detection. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part IX , pages 280±296. Springer, 2022.

F. Liang, B. Wu, X. Dai, K. Li, Y . Zhao, H. Zhang, P. Zhang, P. Vajda, and D. Marculescu. Open-vocabulary semantic segmentation with mask-adapted clip. arXiv preprint arXiv:2210.04150 , 2022.

T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision , pages 2980±2988, 2017.

T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 , pages 740±755. Springer, 2014.

J. Liu, H. Ding, Z. Cai, Y . Zhang, R. K. Satzoda, V . Mahadevan, and R. Manmatha. Polyformer: Referring image segmentation as sequential polygon generation. arXiv preprint arXiv:2302.07387 , 2023.

S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499 , 2023.

J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 3431±3440, 2015.

I. Loshchilov and F. Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations , 2017.

R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille. The role of context for object detection and semantic segmentation in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 891±898, 2014.

L. Muchen and S. Leonid. Referring transformer: A one-step approach to multi-task visual grounding. In Thirty-Fifth Conference on Neural Information Processing Systems , 2021.

V. K. Nagaraja, V. I. Morariu, and L. S. Davis. Modeling context between objects for referring expression understanding. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV 14 , pages 792±807. Springer, 2016.

R. Nock and F. Nielsen. Statistical region merging. IEEE Transactions on pattern analysis and machine intelligence , 26(11):1452±1458, 2004.

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning , pages 8748±8763. PMLR, 2021.

Y. Rao, W. Zhao, G. Chen, Y. Tang, Z. Zhu, G. Huang, J. Zhou, and J. Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 18082±18091, 2022.

H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese. Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 658±666, 2019.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models, 2021.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 10684±10695, 2022.

S. Shao, Z. Li, T. Zhang, C. Peng, G. Yu, X. Zhang, J. Li, and J. Sun. Objects365: A large-scale, highquality dataset for object detection. In Proceedings of the IEEE/CVF international conference on computer vision , pages 8430±8439, 2019.

C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. Jorge Cardoso. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3 , pages 240±248. Springer, 2017.

R. Szeliski. Computer vision: algorithms and applications . Springer Nature, 2022.

Z. Wang, Y. Lu, Q. Li, X. Tao, Y. Guo, M. Gong, and T. Liu. Cris: Clip-driven referring image segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 11686± 11695, 2022.

J. Wu, X. Li, X. Li, H. Ding, Y. Tong, and D. Tao. Towards robust referring image segmentation. arXiv preprint arXiv:2209.09554 , 2022.

Y. Xian, S. Choudhury, Y. He, B. Schiele, and Z. Akata. Semantic projection network for zero-and few-label semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 8256±8265, 2019.

J. Xu, S. De Mello, S. Liu, W. Byeon, T. Breuel, J. Kautz, and X. Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 18134±18144, 2022.

J. Xu, J. Hou, Y. Zhang, R. Feng, Y. Wang, Y. Qiao, and W. Xie. Learning open-vocabulary semantic segmentation models from natural language supervision. arXiv preprint arXiv:2301.09121 , 2023.

J. Xu, S. Liu, A. Vahdat, W. Byeon, X. Wang, and S. De Mello. Open-vocabulary panoptic segmentation with text-to-image diffusion models. arXiv preprint arXiv:2303.04803 , 2023.

M. Xu, Z. Zhang, F. Wei, Y. Lin, Y. Cao, H. Hu, and X. Bai. A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXIX , pages 736±753. Springer, 2022.

B. Yan, Y. Jiang, J. Wu, D. Wang, P. Luo, Z. Yuan, and H. Lu. Universal instance perception as object discovery and retrieval. arXiv preprint arXiv:2303.06674 , 2023.

Z. Yang, J. Wang, Y. Tang, K. Chen, H. Zhao, and P. H. Torr. Lavt: Language-aware vision transformer for referring image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 18155±18165, 2022.

L. Yu, Z. Lin, X. Shen, J. Yang, X. Lu, M. Bansal, and T. L. Berg. Mattnet: Modular attention network for referring expression comprehension. In CVPR , 2018.

L. Yu, P. Poirson, S. Yang, A. C. Berg, and T. L. Berg. Modeling context in referring expressions. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14 , pages 69±85. Springer, 2016.

H. Zhang, F. Li, S. Liu, L. Zhang, H. Su, J. Zhu, L. M. Ni, and H.-Y. Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 , 2022.

W. Zhao, Y. Rao, Z. Liu, B. Liu, J. Zhou, and J. Lu. Unleashing text-to-image diffusion models for visual perception. arXiv preprint arXiv:2303.02153 , 2023.

B. Zhou, H. Zhao, X. Puig, T. Xiao, S. Fidler, A. Barriuso, and A. Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision , 127:302±321, 2019.

X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai. Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 , 2020.

X. Zou, Z.-Y. Dou, J. Yang, Z. Gan, L. Li, C. Li, X. Dai, H. Behl, J. Wang, L. Yuan, et al. Generalized decoding for pixel, image, and language. arXiv preprint arXiv:2212.11270 , 2022.

X. Zou, J. Yang, H. Zhang, F. Li, L. Li, J. Gao, and Y. J. Lee. Segment everything everywhere all at once. arXiv preprint arXiv:2304.06718 , 2023.

Appendix

A.1 List of Datasets

We report the statistics of datasets used in training and evaluation in table Table A1. Additionally, we further evaluate our model on 35 object detection datasets and 25 segmentation datasets in Sec. A.4.2. In total, we benchmarked our model on around 70 datasets. These benchmarks show our model can adapt to many different scenarios and retain a reasonable performance in a zero-shot manner.

A.2 Hierarchical Segmentation

HIPIE

UNINEXT

ODISE

BERT

BERT

CLIP

ceiling

wing

四四

cowtail

airplane

wall

dogleg

cat

persontorso

person

ear

personhead

cat

tail

torso

person

T2

nose

head

eye

head

(person

Fig. A1 highlights the difference of our approach with other methods for hierarchical segmentation. We concatenate class names of different hierarchies as prompts. During the training, we uniquely contrast a mask embedding with both scene-level and part-level labels explicitly. Previous works such as UNINEXT and ODISE only treat these classes as normal multi-word labels. While UNINEXT allows contrasting different words individually because of the design of BERT encoder, it leads to suboptimal signals. In the example above, "person head" has both positive and negative target for "person".

A.3 Experiment Setup

A.3.1 Model Learning Settings

HIPIE is first pre-trained on Objects365 [48] for 340k iterations, using a batch size of 64 and a learning rate of 0.0002, and the learning rate is dropped by a factor of 10 after the 90th percentile of the schedule. After the pre-training stage, we finetune HIPIE on COCO [34], RefCOCO, RefCOCOg, and RefCOCO+ [41, 61] jointly for 120k iterations, using a batch size of 32 and a learning rate of 0.0002. For both stages, we resize the original images so that the shortest side is at least 800

pixels and at most 1024 pixels, while the longest side is at most 1333 pixels. For part segmentation, we train additionally train our model jointly on Pascal-Panoptic-Parts [5] dataset and all previously mentioned datasets. Because of potential data leaks between Pascal-Panoptic-Parts and other Pascal datasets used in the open-vocabulary segmentation evaluation, we report those numbers with weights not trained on Pascal-Panoptic-Part dataset. Because of our hierarchal design, our model produces better-quality masks. In particular, our model can generalize to novel hierarchies that do not exist in part segmentation datasets. In Fig. 6, we provide visualization of such results.

A.3.2 Implementation Details

For loss functions in Eq. (3), we have λ cls = 2 . 0 , λ mask = 5 . 0 , λ box = 5 . 0 , λ ce = 1 . 0 , λ dice = 1 . 0 , λ L 1 = 1 . 0 , λ giou = 0 . 2 . For λ in Eq. (4), we use λ = 0 . 2 for seen classes during the training and λ = 0 . 45 for novel classes. In close-set evaluation, we set λ = 0 . 0 and do not use CLIP. We also do not use CLIP for PAS-21 evaluation (whose classes are mostly covered by COCO) because we find it degrades the performance. We use 800 and 1024-resolution images during the training. For evaluations, we use 1024-resolution images.

A.3.3 Training Process

We train all our models on NVIDIA-A100 GPUs with a batch size of 2 per GPU using AdamW [38] optimizer. We use a base learning rate of 0.0001 and a weight decay of 0.01. The learning rate of the backbone is further multiplied by 0.1. Following UNINEXT [58], We first pretrain our model for object detection on Object365 for 340k iteration (Stage I). Then we fine-tune our model jointly on COCO for object detection, instance segmentation, referring expression comprehension (REC), and referring segmentation (RIS) for 91k iteration (Stage II). We further jointly train our model on Panoptic Segmentation, REC, RIS, and Part Segmentation for 150k iteration (Stage III). In Stage I, the learning rate is dropped by a factor of 10 after 312k iterations. In stage II, the learning rate is dropped by a factor of 10 after 77k iterations. In Stage III, the learning rate is dropped by a factor of 10 after 100k and 135k iterations. In all stages, we sample uniformly across datasets when there are multiple datasets. The global batch size is 64 in Stage I and 32 in Stage II and III. Notably, our stage I and II is identical to the setup of UNINEXT. For ablation studies, we train stage III only and reduce the schedule to 90k iterations. The learning rate schedule is also scaled accordingly. The details of training recipe is shown in Table A2.

A.4 Additional Evaluations

A.4.1 Referring Expression Comprehension

In addition to Referring Segmentation reported in Table 7, we further report results on Referring Expression Comprehension (REC), which aims to locate a target object in an image at the pixel-level, given a referring expression as input. We establish new state-of-the-art performance by an average of +0 . 3 P@0.5 and +0 . 5 oIoU across three datasets.

Method

Mean

Median

AerialMaritime

Drone large

AerialMaritime

Drone tiled

American Sign

Lang Letters

Aquarium

BCCD

boggleBoards

brackish

Underwater

ChessPieces

Cottontail

Rabbits

dice

mediumColor

DroneControl

EgoHands

generic

EgoHands

specific

HardHat

Workers

Mask Wearing

Mountain Dew

Commercial

North America

Mushrooms

open Poetry

Vision

Oxford Pets

by-breed

Oxford Pets

by-species

Packages

Pascal VOC

Pistols

PKLot

plantdoc

Pothole

Raccoon

selfdriving Car

Shellfish

OpenImages

Thermal

Cheetah

thermal Dogs

And People

Uno Cards

Vehicles

OpenImages

website

Screenshots

MDETR10.7 3.0

0.6

5.4

0.3

1.7 6.70.0 0.7 3.066.5 0.0 3.8 5.9

3.5

0.4 0.4 3.0 39.8 0.0

0.0

0.7 63.6 5.6 15.90.00.5

12.750.6

2.8 8.1

4.5

42.80.013.4

0.7

12.5

GLIP-T

11.4

1.6

8.3

17.1

0.1 16.01.70.0 1.7 0.057.0 0.5 0.1 1.1

0.1

2.7

0.615.3 5.9

0.0

0.3

1.6 58.351.231.60.01.6 1.6

6.2

7.4

15.9 0.2 38.70.0

55.0

0.3

0.0

HIPIE

†

14.5

3.9

5.2

9.6

2.9

8.6 6.00.0 0.9 3.869.5

0.5

0.7 5.8

0.2

1.4 0.8

37.7

27.4 0.0

7.8

2.5

68.1

58.636.41.1

3.7

3.9 33.45.327.5 0.5 24.50.053.9 0.3

0.0

HIPIE

‡

17.9

5.5

10.9

16.6 2.8

18.38.00.1 2.7 5.575.7

0.3

1.6 6.6

0.5

1.8

1.1

8.5

42.7

0.0

7.2

2.7

56.2

66.066.82.6

3.6 2.9 49.77.3

49.6

0.3

53.3

0.053.5 0.4

0.3

Method

Mean Median

Airplane

Parts

Bottles

Brain

Tumor

Chicken

Cows

Electric

Shaver

Elephants

Fruits

Garbage

Ginger

Hand

Hand

Metal

House

Parts

HouseHold

Items

Nutterfly

Squireel

Phones

Poles

Puppies

Rail

Salmon

Fillet

Tablets

Toolkits

Trash

Watermelon

X-Decoder(L)

32.3

22.3

13.1 42.1

2.2

8.6

44.9

7.5

66.0

79.2 33.0

11.6 75.9 42.1

7.0

53.0 68.4 15.6 20.1 59.0

2.3

19.0 67.1

22.5

9.9

22.3 13.8

HIPIE (H)

41.2

45.1

14.0 45.1

1.9

46.5 50.1 76.1 68.6

61.1 31.2

24.3 94.2 64.0

6.8

53.4 79.7

7.0

6.7

64.6

2.2

41.8 81.5

8.8

17.9 31.2 50.6

A.4.2 Object Detection and Segmentation in the Wild

To further examine the open-vocabulary capability of our model, we evaluate it on the Segmentation in the Wild (SeginW) [66] consisting of 25 diverse segmentation datasets and Object Detection in the Wild (OdinW) [29] Benchmark consisting of 35 diverse detection datasets. Since OdinW benchmark contains Pascal VOC and some of the classes in SeginW benchmark are covered by Pascal-Panoptic-Parts, we use a version of our model that is not trained on Pascal-Panoptic-Parts for both benchmarks for a fair comparison.

We report the results in Table A5 and Table A4. Notably, our method establishes a new state-of-the-art of SeginW benchmark by an average of +8 . 9 mAP across 25 datasets. We achieve comparable performance under similar settings. In particular, our ResNet-50 baseline outperforms GLIP-T by +3 . 1 mAP. We note that other methods such as GroundingDINO [36] achieve better absolute performance by introducing more grounding data, which can be critical in datasets whose classes are not common objects. (For example, the classes of Boggle Boards are letters, the classes of UnoCards are numbers, and the classes of websiteScreenshots are UI elements).

A.5 Other Ablation Studies

We provide further ablations on a few design choices in this section.

Text Encoder . We experiment with replacing the BERT text encoder in UNINEXT with a pre-trained CLIP encoder. Additionally, following practices of ODISE [56], we prompt each label to a sentence "a photo of <label>". For RIS and REC tasks, the language expression remains unchanged. We report

the result in Table A6. We find that while CLIP and BERT achieve similar performance on panoptic and instance segmentation, BERT performs significantly better on referring instance segmentation ( +28 . 6 oIoU). We hypothesize that this may be caused by the lack of explicit language-focused training which can help achieve a better understanding of referring expression.

SimOTA .Following UNINEXT [58] we adopted simOTA in the matching process for "thing" classes during the training. We experiment with removing simOTA matching and use standard one-to-one matching instead. We report the result in Table A7. We find that simOTA improves the performance on both panoptic segmentation and referring instance segmentation.

A.6 Limitations

We've showcased experimental evidence supporting our method across a diverse set of tasks, including open vocabulary panoptic and semantic segmentation, instance and referring segmentation, and object detection. However, it will be crucial for future work to test our methodology on video-related tasks, such as object tracking and segmentation, to draw comparisons with other universal models like UNINEXT [58]. Furthermore, it's worth considering additional pretraining of our vision encoder on newer, more complex datasets that encompass a vast amount of masks and information. For instance, SA-1B [24], which includes over 1 billion masks, would serve as an ideal training ground. Lastly, it would be advantageous to measure the change in performance when training on supplementary hierarchical datasets. Such an approach will allow us to demonstrate more varied object part segmentations, thereby expanding the capabilities and versatility of our model.

A.7 Broader Impact

Our research introduces a potent approach to hierarchical and universal open vocabulary image segmentation, aiming to address the ever-increasing demand for more data and advanced model architectures. As the demand increases, practical methodologies such as universal segmentation are projected to play a vital role in constructing and training foundational models. Our model, HIPIE, shows promise for fostering progress in a multitude of fields where hierarchical data are critical, including self-driving cars, manufacturing, and medicine. However, it's imperative to acknowledge potential limitations. Given that our model is trained on human annotations and feedback, it can inadvertently replicate any errors or biases present in the datasets. The architecture's complexity is further enhanced when multiple models are integrated, which, in turn, compromises the explainability of the final predictions. Therefore, as with the introduction of any novel technology, it's crucial to implement safety protocols to mitigate misuse. This includes mechanisms for ensuring the accuracy of inputs and establishing procedures to comprehend the criteria the model employs for predictions. By doing so, we can improve the model's reliability and mitigate potential issues.

A.8 Qualitative Results

A.8.1 More Visualizations

We provide more visualizations of panoptic segmentation, part segmentation and referring segmentation in Figs. A2 and A3.

A.8.2 Combining with SAM

We integrate our model with the mask outputs generated by the ViT-H Image encoder from Segment Anything (SAM) [24]. The encoder is trained on SA-1B which encompasses a broad spectrum of objects and masks within each image, enabling us to enhance our segmentation output by utilizing the high-quality masks from the SAM encoder to generate finer, more detailed masks.

To elaborate, in the context of panoptic segmentation, we implement a voting scheme between our pixel-wise annotations and the masks from Segment Anything (SAM), enriching these masks with our labels. For objects where our model demonstrates a strong understanding of hierarchy, such as "person" or "bird", we substitute the SAM masks with ours. This approach enables us to optimize hierarchical outcomes in the face of highly complex images.

Based on our observations from the figures, it's evident that Grounding DINO generates instance segmentation bounding boxes and subsequently uses SAM for the application of the segmentation masks. While this method proves effective for most datasets, SA-1B is a highly complex set featuring a vast array of whole objects, parts and subparts. Our qualitative findings suggest that the a single granularity instance segmentation model may fail to fully capture all objects/parts within an image or may incorrectly identify them. This consequently leads to SAM receiving sub-optimal bounding boxes for segmentation, resulting in fewer and less accurate masks (see third columns in Figs. A4 to A6). In contrast, our methodology (see last columns in Figs. A4 to A6) integrates the SAM encoder masks with our annotations and hierarchical masks wherever feasible. This results in a significantly more fine-grained and accurate output, proving superior in handling complex datasets such as SA-1B.

A.8.3 Combining with Stable Diffusion

As an interesting experiment, we combined our model with image generation model StableDiffusion[46] in Fig. A7. Given a source expression and target prompt, we first use HIPIE's segmentation capability to find the corresponding masks, which are then used for image inpainting. Notably, our model can uniquely achieve fine-grained control over object parts by providing part segmentation masks.

Panoptic Segment

Part Segment

Subpart Segment

Referring Segment

rine glase

curtain

do0

head

mountain

Panoptic Segment

Part Segment

Subpart Segment

Referring Segment

oindow-other

donut

dno

Raw Image

GDINO+SAM

HIPIE+SAM

Raw Image

GDINO+SAM

HIPIE+SAM

和

Raw Image

GDINO+SAM

HIPIE+SAM

Segmentation

Generation

Prompt

Peer reviews:
comments: SUMMARY:
In this paper, the authors target  open-vocabulary setting and propose a universal framework for open-vocabulary semantic/instance/panoptic segmentation.  The whole framework is DETR-like. And to deal with the discrepancies between the thing and stuff classes, the authors ultilize independent decoders for thing and stuff classes. The authors benchmark their method on various datasets and achieve remarkable performance.

STRENGTHS:
1) The idea is simple and easy to understand.
2) The results seem promising.

WEAKNESSES:
1) The idea seems incremental. Compared to UNITEXT, little new knowledge is introduced. The authors try to claim that their paper focuses on the open-vocabulary setting, while UNITEXT does not. However, they do not make it clear what prevents the extension of UNITEXT to the open-vocabulary setting, and their particular design for such setting. In fact, as noted on UNITEXT's github project homepage, the approach extends easily to open-vocabulary setting and achieves better performance than the proposed approach on the Seginw benchmark.

2) The comparison with other open-vocabulary semantic/panoptic segmentation methods is unfair. Since the authors used many datasets with much overlap with ADE20k-150 or Pascal Context 59 for training, but not in the previous methods, I suspect that the performance gain achieved by the proposed method comes from the training dataset. The authors should evaluate their method on a harder dataset (e.g., ADE20K-full) and perform an analysis of the overlap between their training dataset and the test data.

QUESTIONS:
See weakness.

LIMITATIONS:
NaN
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 2
DATE: 2023-06-26

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents HIPIE, an open vocabulary image segmentation model that produces segmentation from text prompts. The authors propose to decouple the segmentation of “thing” and “stuff” due to the differences in their semantic and geometric properties. By training on an additional part-level dataset, HIPIE can also perform part-level segmentation. The authors perform experiments on several open-vocabulary segmentation and referring image segmentation datasets and achieve better performances than previous state-of-the-art methods.\n\nSTRENGTHS:\n- The decoupling of thing and stuff decoding makes sense because of the different feature distributions between the thing classes and the stuff classes. The authors have also experimentally verified that decoupling helps in quantitative measures.\n\n- The proposed method has strong performance on several popular datasets including COCO panoptic segmentation, ADE20K, the referring COCO dataset, etc. \n\n- The authors show that the proposed method can also work on part-level segmentation after being trained on part segmentation datasets. They show that the proposed method works better than Grounding DINO on at least one example in part segmentation which is an interesting application.\n\nWEAKNESSES:\n- The paper claims a hierarchical representation which I find weak. There is only a small paragraph (Section 3.7) about hierarchical segmentation, and it is about part-level segmentation. From the text, it seems like the hierarchy is embedded in the text prompt (e.g., a head consists of ears, hair, etc.) – which any model that works on a text prompt is already capable of incorporating. The proposed model itself is not hierarchical. One thing that the proposed model does while prior work doesn’t is that it trains on part segmentation dataset (Pascal-Panoptic-Parts). However, it seems that the authors have only tested part segmentation performance on the same dataset, hence not demonstrating the “open vocabulary” capability. Overall, I find the claim of “hierarchical model” as a contribution confusing.\n\n- Most of the performance improvements seem to come from decoupling. It appears to me that decoupling leads to extra model parameters and run-time because there are two decoders to run for each input image. There are no comparisons with recent models in these two regards.\n\n- It is unclear why “feature fusion” should not be performed for the “stuff” branch.\n\nQUESTIONS:\nTable 3 – what is the difference between the top part and the bottom part?\n\nLIMITATIONS:\nThe authors present a section for limitations (Section A5) in the supplementary material. It points to several future work directions rather than limitations of the proposed model. One potential limitation is that the part-level segmentation might not generalize to vocabulary beyond the training set.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a unified method for open-vocabulary universal image segmentation and detection methods. A text-image fusion module takes both the image features and text features and then sends the fused results to the decoder. Several designed choices are presented and compared here. The model utilizes the dataset of Objects365, COCO, RefCOCO, RefCOCOg and RefCOCO+ for training and then test on different benchmarks.\n\nSTRENGTHS:\n1. The overall method is clear and easy to understand.\n2. The motivation to unify all the open-vocabulary segmentation and detection tasks is good.\n\nWEAKNESSES:\n1. The model is first pretrained on Object365 which has over 600K well-labeled images and then finetuned on COCO, RefCOCO, RefCOCOg and RefCOCO+. Previous open-vocabulary image segmentation methods like [1-3] only trains on COCO dataset. I don't think the comparison is fair as the model has seen much more well-labeled datasets.\n2. Since the hierarchical segmentation only requires the change of text prompts, I think all previous work can also perform on this task. Maybe the authors need to tone down the hierarchical thing and add some comparison with previous work.\n3. The paper is titled open-vocabulary, however, lots of results in the paper are actually not such as the COCO results in Table 2, 5, 6. It's ok to report the results, but the authors need to report the open-vocabulary results which should be the main focus.\n4. Some important comparisons are missing [4, 5] on open-vocabulary semantic segmentation.\n\n[1] Scaling Open-Vocabulary Image Segmentation with Image-Level Labels.\\\n[2] Open-vocabulary panoptic segmentation with maskclip.\\\n[3] Open-vocabulary panoptic segmentation with text-to-image diffusion models.\\\n[4] Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP.\\\n[5] Side Adapter Network for Open-Vocabulary Semantic Segmentation.\n\nQUESTIONS:\nTable 3 seems confusing, why do the authors split the table into two parts?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2023-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper propose to disentangle the representation learning and decoding for things and stuff and unify multiple segmentation tasks with different granularity (whole, part, subpart) and text formulation (reference text or category only). Extensive experiments are carried out to validate its effectiveness.\n\nSTRENGTHS:\n1. The paper proposed to unifying different segmentation tasks and benchmark them, which is a promissing direction for future researches.\n\n2. The decoupling of things and stuffs in representation learning are intuitive.\n\n3. The experimental results are extensive and promissing.\n\nWEAKNESSES:\n1. As the model only uses fully supervised data for supervision without large-scale image-text pretraining, whether its open-vocabulary capability is enough to be claimed as a open-vocabulary model? I am looking forward to see more open-vocabulary results that a quite different with exisitng training vocabulary with and without the assistance of CLIP for inference.\n\n2.  It seems that the architecture designs (decoupling thing and stuff) of this work do not feasible to the whole/part/subpart seg. If previous methods such as UNINEXT, X-Decoder, SEEM  are also trained with part segmentation dataset (e.g., Pascal-Panoptic-Part). Can it already provide a solid results for such unified segmentation? Better with some experimental results.\n\nQUESTIONS:\nGenerally, I appreciate the setting of this work, but still have some concerns as detailed in the weakness part. If the author can address my concern, I am willing to upgrade my rating.\n\nBesides, I hope the author can have a detailed introduction about how to do the hierarchical segmentation in 3.7, you need to do the segmentation multiple times or just once? If the text query has both whole, part and subpart categories, how will the model do the inference.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-20"}