Title: DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut
Venue: NeurIPS 2024
Accepted: yes
Keywords: Segmentation

Image Segmentation

Zero-Shot Segmentation

Diffusion Features

Normalized Cut
Abstract: Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks. While prior works have addressed unsupervised semantic segmentation, they significantly lag behind supervised models. In this paper, we use a diffusion UNet encoder as a foundation vision encoder and introduce DiffCut, an unsupervised zero-shot segmentation method that solely harnesses the output features from the final self-attention block. Through extensive experimentation, we demonstrate that using these diffusion features in a graph based segmentation algorithm, significantly outperforms previous state-of-the-art methods on zero-shot segmentation. Specifically, we leverage a recursive Normalized Cut algorithm that regulates the granularity of detected objects and produces well-defined segmentation maps that precisely capture intricate image details. Our work highlights the remarkably accurate semantic knowledge embedded within diffusion UNet encoders that could then serve as foundation vision encoders for downstream tasks.

DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut

Abstract

Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks. While prior works have addressed unsupervised semantic segmentation, they significantly lag behind supervised models. In this paper, we use a diffusion UNet encoder as a foundation vision encoder and introduce DiffCut, an unsupervised zero-shot segmentation method that solely harnesses the output features from the final self-attention block. Through extensive experimentation, we demonstrate that using these diffusion features in a graph based segmentation algorithm, significantly outperforms previous state-of-the-art methods on zero-shot segmentation. Specifically, we leverage a recursive Normalized Cut algorithm that regulates the granularity of detected objects and produces well-defined segmentation maps that precisely capture intricate image details. Our work highlights the remarkably accurate semantic knowledge embedded within diffusion UNet encoders that could then serve as foundation vision encoders for downstream tasks. Project page : https://diffcut-segmentation.github.io

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DiffSeg

1 Introduction

Foundation models have emerged as powerful tools across various domains, including language [2, 3, 4], vision [5, 6, 7], and multimodal tasks [8, 9, 10, 11, 12, 13]. Pretrained on extensive datasets, these models exhibit unparalleled generalization capabilities, marking a significant departure from training models from scratch to efficiently adapting pretrained foundation models [14, 15, 16, 17]. Utilizing pretrained models is particularly vital for dense visual tasks, alleviating the need for large annotated datasets specific to each domain. While prior works [18, 19, 20, 21, 22] have addressed unsupervised image segmentation, they significantly lag behind supervised models [23, 24, 25, 26]. Recently, SAM [27], proposed a model that can produce fine-grained class-agnostic masks which achieves outstanding zero-shot transfer to any images. Still, it requires a huge annotated segmentation dataset as well as significant training resources. Therefore, in this work, we investigate an alternative direction: unsupervised and zero-shot segmentation under the most constraining conditions, where no segmentation annotations or prior knowledge on the target dataset are available.

Recently, several methods have emerged to address unsupervised object detection by framing it as a graph partitioning problem, utilizing self-supervised ViT features [28, 5]. LOST [29] proposes to localize a unique object in a image by exploiting the inverse degree information to find a seed patch. TokenCut [30] splits the graph in two subsets given a bipartition. FOUND [31] and MaskCut [32] extend these approaches by addressing the single object discovery limitation. While being able to localize multiple objects, the latter methods remain constrained to identify a pre-determined number of objects, making them ill-suited for a task of unsupervised image segmentation which inherently requires to adapt the number of segment to uncover to the visual content.

In this work, we introduce DiffCut, a new method for zero-shot image segmentation which solely harnesses the encoder features of a pre-trained diffusion model in a recursive graph partitioning algorithm to produce fine-grained segmentation maps. Importantly, our method does not require any label from downstream segmentation datasets and its backbone has not been pre-trained on dense pixel annotations such as SAM [27]. We observe in Fig. 1 that DiffCut produces sharp segments that nicely outline object boundaries. In comparison with the recent state-of-the-art unsupervised zero-shot segmentation method DiffSeg [1], the segments yielded by DiffCut, are better aligned with the semantic visual concepts, e.g. DiffCut is able to uncover the urban area as well as the boats in the middle row image. Our main contributions are as follows:

We leverage the features from the final self-attention block of a diffusion UNet encoder, for the task of unsupervised image segmentation. In this context, we demonstrate that exploiting the inner patch-level alignment yields superior performance compared to merging self-attention maps as done in DiffSeg [1].

Compared to existing graph based object localization methods e.g. TokenCut or MaskCut [30, 32], we push further and take advantage of a recursive Normalized Cut algorithm to generate dense segmentation maps. Via a partitioning threshold, the method is able to regulate the granularity of detected objects and consequently adapt the number of segments to the visual content.

We perform extensive experiments to validate the effectiveness of DiffCut and show that it significantly outperforms state-of-the-art methods for unsupervised segmentation on standard benchmarks, reducing the gap with fully supervised models.

In addition, we exhibit the remarkable semantic coherence emerging in our chosen diffusion features by measuring their patch-level alignment, which surpasses other backbones such as CLIP [8] or

DINOv2 [5]. Our ablation studies further reveal the relevance of these diffusion features as well as the recursive partitioning approach which proves to provide robust segmentation performance. Finally, we show that DiffCut can be extended to an open-vocabulary setting with a straightforward process leveraging a convolutional CLIP, which even tops most dedicated methods on this task.

2 Related Work

Semantic segmentation. Semantic segmentation consists in partitioning an image into a set of segments, each corresponding to a specific semantic concept. While supervised semantic segmentation has been widely explored [45, 46, 47, 27], unsupervised and zero-shot transfer segmentation for any images with previously unseen categories remains significantly more challenging and much less investigated. For example, most works in unsupervised segmentation require access to the target data for unsupervised adaption [21, 20, 19, 18]. Therefore, these methods cannot segment images that are not seen during the adaptation. Recently, DiffSeg [1] moved a step forward by proposing an unsupervised and zero-shot approach that can produce quality segmentation maps without any prior knowledge on the underlying visual content.

Segmentation with Text Supervision. Recent works have shown that learning accurate segmentation maps is possible with text supervision, overcoming the cost of dense annotations. These works are mostly based on image-text contrastive learning [48, 49, 50, 51], and usually exploit the features of CLIP [52, 53, 54]. MaskCLIP [52] leverages CLIP to get pseudo labels used to train a typical image segmentation model. ReCO [53] uses CLIP for dataset curation and get a reference image embedding for each class that is used to obtain the final segmentation. CLIPpy [48] proposes minimal modifications to CLIP to get dense labels. SegCLIP [54] continues to train CLIP with additional reconstruction and superpixel-based KL loss to enhance localization. TCL [50] learns a region-text alignment to get precise segmentation masks. GroupViT [49] also learns masks from text supervision and is based on a hierarchical grouping mechanism. Similarly, ViewCo [51] proposes a contrastive learning between multiple views/crops of the image and the text.

Graph-based Object Detection. Built on top of self-supervised ViT features, various methods frame the problem of object detection as a graph partitioning problem. LOST [29] aims at detecting salient object in an image using the degree of the nodes in the graph and a seed expansion mechanism. Based on Normalized Cut (NCut) [55], FOUND [31] proposes to identify all background patches, hence discovering all object patches as a by-product with no need for a prior knowledge of the number of objects or their relative size with respect to the background. TokenCut [30] detects one single salient object in each image with a unique NCut bipartition. In an attempt to adapt TokenCut to multi-objects localization, MaskCut [32] first localizes an object and disconnects its corresponding patches to the rest of the graph before repeating the process a pre-determined number of times. As these graph partitioning methods are only able to uncover a fixed number of segments, they are inadequate for a task of image segmentation.

3 DiffCut

Diffusion Models. Diffusion models [60, 61, 62] are generative models that aim to approximate a data distribution q by mapping an input noise x T ∼ N (0 , I ) to a clean sample x 0 ∼ q through an iterative denoising process. In latent text-to-image (T2I) diffusion models, e.g. Stable Diffusion [33], the diffusion process is performed in the latent space of a Variational AutoEncoder [63] for computational efficiency, and encode the textual inputs as feature vectors from pretrained language models. Starting from a noised latent vector z t at the timestep t , a denoising autoencoder ϵ θ is trained to predict the noise ϵ that is added to the latent z , conditioned on the text prompt c . The training objective writes:

where t is uniformly sampled from the set of timesteps { 1 , . . . , T } .

Diffusion

UNet

Encoder

Affinity matrix

Bilinear

Upsampling

Recursive

NCut

Masked

SMM

Features Extraction

Recursive Feature Clustering

High-Resolution Concept Assignment

3.1 Features Extraction

An input image is encoded into a latent via the VQ-encoder of the latent diffusion model and a small amount of gaussian noise is added to it (not shown in Fig. 2). The obtained latent is passed to the diffusion UNet encoder, from which we only extract the output features, denoted ˆ z , from its last self-attention block. This choice design has several motivations:

Attention Limitations. In contrast to several methods that harness cross-attention modules for localization or segmentation tasks [39, 40, 41, 42], we deliberately choose not to depend on this mechanism. The accuracy of segmentation maps generated via attention modules heavily relies on the quality of the textual input which often requires an automatic captioning model combined with a meticulous prompt design to reach competitive performance. Besides being constrained by the maximum number of input tokens, such approach is proved to be inaccurate in the presence of cohyponyms [64] and is prone to neglect subject tokens as the number of objects to detect becomes large [65]. The localization and segmentation capacity with a single forward inference is then constrained by the performance of the captioning model and the attention modules themselves. Exploiting only the intermediate diffusion features alleviate the computational cost of an additional captioning model and do not necessitate multiple forward inferences.

UNet Encoder Effectiveness. Previous works [66, 67, 37] have shown that diffusion features provide precise semantic information shared across objects from different domains. Building on this observation, we hypothesize that the pyramidal architecture of the UNet encoder capture semantically rich image representations that are well-suited for zero-shot vision tasks. To validate this assumption, we exhibit the semantic coherence emerging in the UNet encoder, evidenced by a remarkable patch level alignment in the output features of the final self-attention block. We in fact demonstrate that these features are sufficient to reach state-of-the-art zero-shot segmentation performance.

Computational Efficiency. By solely exploiting the diffusion UNet encoder, our method offers a substantial computational gain, reducing the model size by 70% (400M vs 1.3B parameters). In contrast, DiffSeg extracts every self-attention maps of the UNet which requires a full model inference.

3.2 Recursive Feature Clustering

Normalized Cut treats image segmentation as a graph partitioning problem [55]. Given a graph G = ( V , E ) where V and E are respectively a set of nodes and edges, we construct an affinity matrix W such that W ij is the edge between node v i and v j , and a diagonal degree matrix D , with d ( i ) = ∑ j W ij . NCut minimizes the cost of partitioning the graph into two sub-graphs by solving:

to find the eigenvector x corresponding to the second smallest eigenvalue. In the ideal case, the clustering solution only takes two discrete values. Since the solution of Eq. (2) is a continuous relaxation of the initial problem, x contains continuous values and a splitting point has to be determined to partition it. To find the optimal partition, we examine l evenly spaced points in x and select the one resulting in the minimum NCut value.

Graph Affinity. Based on our observations of the patch-level alignment of diffusion features illustrated in Fig. 4, we assume that the normalized cut algorithm will produce sharp segments, each corresponding to a precise semantic concept as distinct objects would manifest as weakly connected components in a patch similarity matrix. Following this intuition, we construct an affinity matrix W , by computing the cosine similarity, normalized between 0 and 1, between each pair of patches. As the NCut criterion evaluates both the dissimilarity between different segments and the similarity within each segment, we opt to emphasize inter-segments dissimilarity by raising each element to a positive integer power α :

Essentially, this process maintains a relatively high affinity for highly similar patches, while squashing the weights between dissimilar patches towards zero. This mechanism plays the role of a soft thresholding , offering a more gradual adjustment compared to setting a threshold to explicitly binarize the affinity matrix as done in [30] and [32].

Recursive Partitioning. Classical spectral clustering [68] requires setting a pre-defined number of clusters to partition the graph, which is a significant constraint in the context of zero-shot image segmentation where no prior knowledge on the visual content is available. We therefore adopt a recursive graph partitioning [55], which adapts the number of uncovered segments to the visual content via a threshold, denoted τ , on the maximum partitioning cost. This hyperparameter stops the recursive partitioning of a segment when its NCut value exceeds it and thereby regulates the granularity of detected objects. We demonstrate that our soft thresholding process detailed above enhances the robustness of the method which delivers competitive performance across a wide range of τ values. This recursive clustering process is summarized in Supplementary A.

3.3 High-Resolution Concept Assignment

Thus far, we have constructed segmentation maps ( e.g. 32 × 32 ) which are 32 times lower in resolution than the original image ( e.g. 1024 × 1024 ). The number of segments found in each image depends on the image and the value of the hyperparameter τ . Our next goal is to upscale these low-resolution maps to build accurate pixel-level segmentation maps. We propose a high-resolution segmentation process that can be decomposed into the following steps:

Masked Spatial Marginal Mean. First, our objective is to extract a set of representations that embeds the semantics of each segment. As shown in [37], reducing the spatial dimension of diffusion features with a Spatial Marginal Mean (SMM) effectively retains semantic information and provides accurate image descriptor. In light of this, we naturally propose to collapse the spatial dimension of each segment with a Masked SMM . This process yields a collection of semantically rich concept-embeddings, denoted C .

Concept Assignment. A naive approach to obtain segmentation maps at the original image resolution consists in performing a nearest-neighbor upsampling. Despite its straightforwardness, this approach results in a blocky output structure as all pixels within the same feature patch are assigned to the same concept. Alternatively, we opt to first bilinearly upsample our low-resolution feature map ˆ z to match the original image spatial size and then proceed with the pixel/concept

assignment. Specifically, for each concept c i ∈ C , we compute its cosine similarity with the upsampled features ˆ z up . This yields a similarity matrix of size ( H × W × K ) where K = |C| . Then, the assignment process simply consists in taking the argmax across the K channels. The obtained segmentation map S is eventually refined with a pixel-adaptive refinement module [69].

4 Experiments

Datasets. Following existing works in image segmentation [21, 53, 52, 18], we use the following datasets for evaluation: a) Pascal VOC [70] (20 foreground classes), b) Pascal Context [71] (59 foreground classes), c) COCO-Object [72] (80 foreground classes), d) COCO-Stuff-27 merges the 80 things and 91 stuff categories in COCO-stuff into 27 mid-level categories, e) Cityscapes [73] (27 foreground classes) and f) ADE20K (150 foreground classes) [74]. An extra background class is considered in Pascal VOC, Pascal Context, and COCO-Object. We ignore their training sets and directly evaluate our method on the original validation sets, at the exception of COCO for which we evaluate on the validation split curated by prior works [21, 19].

Metrics. For all datasets, we report the mean intersection over union (mIoU), the most popular evaluation metric for semantic segmentation. Because our method does not provide a semantic label, we use the Hungarian matching algorithm [75] to assign predicted masks to a ground truth mask. For datasets including a background class, we perform a many-to-one matching to the background label (Supplementary H). As in [1], we emphasize unsupervised adaptation (UA) , language dependency (LD) , and auxiliary image (AX) . UA means that the specific method requires unsupervised training on the target dataset. Methods without the UA requirement are considered zero-shot. LD means that the method requires text input, such as a descriptive sentence for the image, to facilitate segmentation. AX means that the method requires an additional pool of reference images or synthetic images.

Implementation details. DiffCut builds on SSD-1B [35], a distilled version of Stable Diffusion XL [34]. The model takes an empty string as input and we set the timestep for denoising to t = 50 . To ensure a fair comparison when evaluating our method against baselines, we set a unique value for τ and α across all datasets, equal to 0 . 5 and 10 respectively. Following previous works, we make use of PAMR [69] to refine our segmentation masks. Our method runs on a single NVIDIA TITAN RTX (24GB) with input images of size 1024 × 1024 and can segment an image in one second.

4.1 Results on Zero-shot Segmentation

Tab. 1 reports the mIoU score for each baseline across the 6 benchmarks. Note that the numbers shown for COCO-Stuff and Cityscapes are taken from [1]. We complete ReCo [53] and MaskCLIP [52] scores with the results obtained in [50]. Other numbers are taken from [18]. We also note that DiffSeg tunes the sensible merging hyperparameter on a subset of images from the training set from the respective datasets. For a fair comparison, we evaluate the method fixing it to 1, as recommended in the original paper, and refine the obtained masks with PAMR. This baseline is denoted DiffSeg † .

With our set of default hyperparameters, DiffCut significantly outperforms all other baselines despite not relying on language dependency, auxiliary images or unsupervised adaptation. On average, our method achieves a gain of +7.3 mIoU over the second best baseline. Notably, DiffCut exceeds MaskCut with an average improvement of +9.4 mIoU. Additionally, it outperforms the previous state-of-the-art method in unsupervised segmentation, DiffSeg, by +5.5 mIoU on COCO-Stuff and +9.4 mIoU on Cityscapes. The superiority of DiffCut over these two methods demonstrates our two key contributions: the high quality of our visual features for semantic segmentation and the flexibility of the recursive NCut algorithm in adjusting the number of segments according to the visual content of each image. The effectiveness of our method is further corroborated by our qualitative results shown in Fig. 1. In comparison to DiffSeg, DiffCut provides finely detailed segmentation maps that more closely align with semantic concepts. Additional examples can be found in Supplementary N.

We note here that, as the granularity of annotations varies across target datasets, our fixed set of hyperparameters can not be in the optimal regime on each of them. Therefore, relaxing the condition on prior knowledge about the target dataset, we report in Supplementary G results of DiffCut where τ is loosely tuned using a small subset of annotated images from the target training split.

4.2 Semantic Coherence in Vision Encoders

As good candidates for a task of unsupervised segmentation are expected to be semantically coherent, we conduct a comparison between different families of foundation models on their internal alignment at the patch-level. Selected models include text-to-image DMs (SSD-1B [35]), text-aligned contrastive models (CLIP [79], SigLIP [80]) and self-supervised models (DINO [28], DINOv2 [5]). At the exception of DINO-ViT-B/16, evaluated models are of roughly similar size, approximately 300M parameters for DINOv2, CLIP-ViT-L/14 and SigLIP-ViT-L/16 and 400M for SSD-1B UNet encoder.

As in [81], we collect patch representations from various vision encoders and store their corresponding target classes using the segmentation labels. Given ˆ z i = E ( x 1 ) i ∈ R D v and ˆ z j = E ( x 2 ) j ∈ R D v , the patch representations of images x 1 and x 2 at respectively index i and j , we compute their cosine similarity and use this score as a binary classifier to predict if the two patches belong to the same class. Given l ( x 1 ) i and l ( x 2 ) j , the labels associated to the patches, if l ( x 1 ) i,j = l ( x 2 ) p,q , the target value for binary classification is 1, else 0. We present in Fig. 3 the ROC curve and AUC score for our candidate models. We observe that SSD-1B UNet encoder [35] demonstrates a greater patch-level alignment than any other candidate model

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DINO-ViT-B/16(AUC=0.59)

DINOv2-ViT-L/14 (AUC=0.79)

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with an AUC score of 0.83, even surpassing DINOv2 [5]. We further exhibit the outstanding alignment between patch representations associated to semantically similar concepts with qualitative results in Fig. 4. We provide additional qualitative examples patch-level alignment in Supplementary M.

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DINOV2-VIT-L/14

CLIP-VIT-L/14

SIGLIP-VIT-L/16

A potential rationale for this observation lies in the superior semantic information retention of a diffusion model compared to alternative backbones, attributed to its inherent capacity to set a structural image layout, internally acquired during the training phase. These results provides insight into the strong clustering results presented in previous section, as improved semantic coherence suggests that patches belonging to the same object are more effectively clustered.

4.3 Ablation study

In this section, we perform ablation studies to validate the individual choices in the design of DiffCut.

DiffCut vs DiffSeg. DiffSeg proposes their own clustering algorithm based on a self-attention map merging process. As the original implementation uses a different diffusion backbone as ours, we validate the benefit of our method by swapping the original SDv1.4 with our stronger SSD-1B. For a fair comparison between methods, we use the default set of hyperparameters recommended in [1] and set the default merging threshold of DiffSeg to 0 . 5 for all datasets. Tab. 2 clearly validates the superiority of using rich semantic features in a recursive graph partitioning algorithm over the self-attention merging mechanism of DiffSeg. Qualitative results shown in Fig. 1 further display the edge of DiffCut in uncovering semantic clusters. Shown results do not make use of the mask refinement module, explaining the gap with Tab. 1.

Recursive Normalized Cut vs Automated Spectral Clustering. In DiffCut, the hyperparameter τ corresponds to the maximum graph partitioning cost allowed. In contrast, classical spectral clustering requires to explicitly set the number of segments to be found in the graph. To validate the benefit of the recursive approach over spectral clustering, we introduce a simple yet effective baseline dubbed AutoSC. [82] proposes a heuristic that estimates the number of connected components in a graph with the largest relative-eigen-gap between its Laplacian eigen-values. The larger the gap, the more confident the heuristic. In our context, the index of the eigen-value that maximizes this quantity can be interpreted as the number of clusters in an image. Thus, we define a set of exponents { 1 , 5 , 10 , 15 } and determine the value α in this set such that its element-wise exponentiation of matrix A yields the largest Laplacian relative-eigen-gap . Then, we use the index of the eigen-value maximizing the gap as the number of clusters in a k -way spectral clustering performed with the algorithm proposed in [83]. As shown in Tab. 2, DiffCut consistently outperforms AutoSC on all datasets, with a gain up to +3 . 5 on ADE20K, at the exception of COCO-Stuff where the latter yields slightly better results. Noting that AutoSC is already a state-of-the-art baseline on most benchmarks, this experiment confirms the relevance of the recursive Normalized Cut to uncover arbitrary numbers of segments.

4.4 Model Analysis

Hyperparameters Impact. In this section, we assess the impact of hyperparameters τ and α over the segmentation performance. We report in Fig. 5 the mIoU for various α values, with respect to partitioning threshold values τ ranging from 0.3 to 0.97 on Cityscapes validation set. As α increases, we observe a dual effect. First, since a greater α value shrinks the affinity matrix components towards 0, the partitioning cost corresponding to the NCut value decreases, explaining the shift of the optimal threshold between the different curves. Second, as α increases, the range of τ values for which the method yields competitive performance widens, contributing to the overall robustness of the method. For example, DiffCut outperforms our

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own competitive baseline AutoSC for any τ between 0.35 and 0.67 when α = 10 , whereas it only surpasses it between 0.92 and 0.96 when α = 1 . Qualitatively, we observe in Fig. 6 that as τ increases, the method uncovers finer segments in images.

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Diffusion Features. Our chosen diffusion backbone uses a UNet-based architecture which consists of an encoder E , bottleneck B and a decoder D . The hierarchical features of the encoder, with spatial resolution of 128 × 128 , 64 × 64 and 32 × 32 respectively, are injected into the decoder D via skip connections. Considering the final self-attention modules at resolution 64 × 64 and 32 × 32 in the encoder and decoder, we demonstrate in Tab. 3, that the encoder's features extracted at the lowest spatial resolution retain the most semantic information and are sufficient to reach optimal performance. In addition, combining different hierarchical features does not lead to any improvements and adds to the computational burden.

Open-Vocabulary Extension. To extend DiffCut to an open-vocabulary setting, we propose in Tab. 4, a straightforward approach that assigns a semantic label to each segmentation mask. After mask proposals are generated, an image is processed by a frozen convolutional CLIP visual encoder, which produces visual representations aligned with text in a shared embedding space via a projection layer. The embedding of each predicted segment is obtained by mask-pooling CLIP visual features, allowing a classification against category text embeddings through contrastive matching. Specifically, let e represent the embedding of a segment, and let { t i } N i =1 denote the text embeddings of category names generated by the pretrained text encoder, the predicted class for this segment is determined as

follows: c = arg max i ∈{ 1 , ··· ,N } softmax ([ cos ( e , t 1 ) , cos ( e , t 2 ) , · · · , cos ( e , t N )]) . This proposed extension reaches competitive performance, even outperforming several baselines dedicated to the task of open-vocabulary zero-shot semantic segmentation.

5 Discussion

In this work, we tackle the challenging task of unsupervised zero-shot semantic segmentation by introducing DiffCut, a method that significantly narrows the performance gap with fully supervised

models. DiffCut leverages the diffusion features of a UNet encoder within a recursive graph partitioning algorithm to generate sharp segmentation maps, achieving state-of-the-art results on popular benchmarks. By reusing pre-trained models in a zero-shot manner, our approach can not only reduce computational resources, energy consumption, and human labor but also align with sustainable AI practices. However, because the diffusion backbone is not specifically trained for specialized domains, such as biomedical imaging, the method may struggle with out-of-distribution images. Fine-tuning the diffusion model on domain-specific data could mitigate this challenge. While fully supervised, end-to-end segmentation methods currently offer better efficiency and accuracy, further advancements could close this gap, positioning diffusion-based UNet encoders as foundation models for future vision tasks.

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DiffCut: Catalyzing Zero-Shot Semantic Segmentation

with Diffusion Features and Recursive Normalized Cut Supplementary Material

A Recursive Normalized Cut on Diffusion Features

We summarize in Algorithm 1 the recursive clustering process used in DiffCut.

Algorithm 1 Recursive Normalized Cut on Diffusion Features

Input:

I an image, τ ∈ ]0 , 1[ a threshold value, α ∈ N ∗ an exponent value.

Step 1: Features Extraction.

Encode the image I with the VAE of the diffusion model: z = E VAE ( I )

Add some gaussian noise to the latent image z .

Pass the noisy latent to the diffusion UNet encoder and extract the output features from its last self-attention block: ˆ z = E UNet ( z )

Step 2: Graph Construction.

Compute the pairwise cosine-similarity between patches of ˆ z to set up a similarity matrix A .

Raise to the power α each element of matrix A to obtain the affinity matrix W (see Eq. (3)).

Determine the matrix degree D .

Step 3: NCut Problem Solving.

Solve ( D -W ) x = λ D x for eigenvector with the second smallest eigenvalue.

Use the eigenvector with the second smallest eigenvalue to bipartition the graph by finding the splitting point such that the NCut value is minimized.

Step 4: Recursive Partitioning.

Store the current partition and retrieve the matrices W and D associated to each sub-graph.

Recursively subdivide the partitions (Step 3) until the NCut value is greater than τ .

Output:

M a segmentation map with the spatial resolution of ˆ z .

B Impact of PAMR

To reveal the effect of the pixel-adaptive refinement module (PAMR) on our method, we compare the segmentation results on all benchmarks both with and without it enabled.

In average, PAMR allows to gain +2.5 mIoU on our segmentation benchmarks. Even though this refinement module helps to better outline the contour of objects, our method still reaches state-of-theart results on unsupervised zero-shot segmentation without it.

C Additional Comparison with MaskCut

DiffCut, is capable of providing dense segmentation maps and dynamically adapting the number of detected segments based on the visual content of an image. In contrast, MaskCut [32] can only detect

a fixed number of segments, making it less suitable for image segmentation. This limitation arises from the use of an iterative graph partitioning approach, where graph nodes associated with detected objects are masked. Each segment is treated as a single object and cannot be refined after detection, which severely restricts its ability to identify a large number of objects. To highlight the superiority of our recursive partitioning over MaskCut's iterative process, we present below a comparison between the two methods with k the number of objects to be detected varying in { 3 , 5 , 20 } .

DiffCut significantly outperforms MaskCut, regardless of the chosen value of k . DiffCut's improvement shows our two key contributions: the effectiveness of our visual features for semantic segmentation and the ability of the recursive NCut algorithm to dynamically adjust the number of segments based on the visual content of each image.

D DiffCut with Alternative Diffusion Backbones

To further display the relevance of diffusion features, we show that DiffCut achieves competitive performance even when using smaller diffusion backbones than SSD-1B. Specifically, we test two alternative models: SD1.4 and SSD-Vega [35] (another distilled version of SDXL). The UNet encoder in SD1.4 has 260M parameters, comprising approximately 30% of the overall UNet, while the UNet encoder in SSD-vega has 240M parameters, making up around 32% of the UNet.

The results obtained using SD1.4 and SSD-Vega are consistent with those achieved with SSD-1B. While the SD1.4 UNet encoder shows a slight performance drop compared to SSD-1B, DiffCut still significantly outperforms DiffSeg. Notably, with the SSD-Vega UNet encoder, DiffCut delivers performance comparable to SSD-1B, despite having only half the number of parameters.

E Mask Upsampling

Normalized Cut algorithm does not scale well with the graph size due to the generalized eigenvalue problem to solve, which hinder its use on the native image resolution ( e.g. , 1024 × 1024 ). Thus, the clustering is applied in the latent space and yields segmentation maps at the latent resolution. To obtain pixellevel segmentation at the original image resolution, we need to

upscale the low-resolution maps. In Tab. 8, we compare the nearest-neighbor upsampling approach versus our concept assignment upsampling and show that our proposed method obtain better results than the naive upsampling of the segmentation masks.

F Visual Encoders KMeans Comparison

To evaluate the potential of vision encoders for zero-shot segmentation, we compare their clustering performance with a simple KMeans algorithm. For selected vision encoders, features are extracted

from the last layer. The hyperparameter K is either determined by the ground-truth ( K ∗ ) for each image, or fixed across the dataset. We compute the mIoU with respect to the ground truth masks using the Hungarian matching algorithm [75]. Tab. 9 shows that the diffusion UNet encoder ( SSD-1B ) significantly outperforms other vision encoders on Pascal VOC (20 classes and no background), COCO-Stuff-27 and Cityscapes. This confirms that diffusion features are well-suited for localizing and segmenting objects. Interestingly, unsupervised models such as DINOv2 are better than CLIP models, suggesting that text-aligned features does not contain accurate localization features.

(a) VOC

(b) COCO-Stuff-27

(c) Cityscapes

G Hyperparameter τ tuning

We show in Sec. 4.3 that the performance of the method is highly robust with respect to the value of the threshold τ . However, as the granularity of annotations varies across target datasets, the value of this threshold, fixed in our experiments, can not be in the optimal regime on each benchmark. Therefore, we relax the condition on the absence of prior knowledge about the target dataset and report in Tab. 10 results of DiffCut where τ is loosely tuned using a small subset of annotated images (200) from the target training split. Specifically, we estimate an adequate value for τ with a grid-search in the set { 0 . 35 , 0 . 55 , 0 . 75 } for COCO-Object, COCO-Stuff and Cityscapes.

As COCO-Object and COCO-Stuff-27 offer different level of object granularity despite corresponding to the same images, a fixed value for τ can not perform optimally on both benchmarks. Tuning the value of this threshold allows to infer the granularity of objects expected to be uncovered in images. For example, the estimated τ ∗ value for COCO-Stuff-27 is 0.35 whereas it is 0.75 on COCO-Object whose annotations requires to detect much finer objects. For Cityscapes the initial fixed τ value was in the good range to yield optimal performance.

H Hungarian Matching

Given a set of predicted masks, our goal is to find the best matching ground-truth labels. For each predicted mask, we compute the Intersection over Union (IoU) with every ground-truth mask and select the one with the highest IoU as its optimal ground-truth match. This results in a pairing where each predicted mask is associated with at most one ground truth mask. In datasets that include a background class, this label implicitly encompasses a variety of concepts related to "things" or "stuff," which vary depending on the dataset. Since our method generates a segment for every detected object in an image, a one-to-one matching between a single predicted cluster and the entire background does not accurately represent the model's true categorization capabilities. Therefore, in such cases, we use a many-to-one matching approach by associating the clusters that primarily overlap with the background to its ID.

I Image Noising

Before passing the image to the diffusion UNet, a predefined amount of gaussian noise, controlled by a parameter called the timestep, is added to it. At timestep t = 0 the input image corresponds to the original image without added noise while t = 1000 corresponds to an image transformed into pure gaussian noise. In Fig. 7, we show the segmentation performance on the validation split of Pascal VOC for timesteps values ranging from 0 to 500. We can observe that a small amount of noise, around 50, gives the best mIoU score, indicating that the best semantic features are obtained with a slightly noisy input image. We note that despite a significant drop in the mIoU score for t = 500 , DiffCut still reaches state-of-the-art segmentation performance on Pascal VOC benchmark, demonstrating a high robustness of the method with respect to the noising ratio.

0

100

200

300

400

500

Timestep

0.54

0.56

0.58

0.60

0.62

mIoU

J Hyperparameter Sensitivity

Fig. 8 presents an additional evaluation of how the hyperparameters α and τ influence the segmentation performance on Pascal VOC. Similar to the observations in Fig. 5, we notice a shift in the optimal threshold across the various curves corresponding to different α values. Besides, DiffCut shows increased robustness across a wide range of τ values, achieving mIoU scores exceeding 60.0 when α = 10 . In contrast, for α = 1 , the mIoU only exceeds 60.0 for τ between 0.91 and 0.94.

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

τ

0.3

0.4

0.5

0.6

mIoU

α

=1

α

=5

α

=10

α

=15

K Runtime Comparison

Tab. 11 presents a runtime comparison between DiffCut, MaskCut, and DiffSeg, which are the main baselines for graph-based image clustering and diffusion-based zero-shot segmentation, respectively.

L Visualization of the effect of τ

REF. IMAGE

τ

= 0

.

3

τ

= 0

.

5

τ

= 0

.

7

VELCOME

HOOE

M Semantic Coherence in Vision Encoders

REF. IMAGE

TARGET IMAGE

REF. IMAGE

TARGET IMAGE

REF. IMAGE

TARGET IMAGE

SSD-1B

DINO-VIT-B/16

DINOV2-VIT-L/14

CLIP-VIT-L/14

SIGLIP-VIT-L/16

N Additional Visualization

DINA

ICT|OUTSOURCIR

acr

HACA

WCENTRE ST.

STOP

O Datasets Licenses

Pascal VOC:

http://host.robots.ox.ac.uk/pascal/VOC/

Pascal Context:

https://www.cs.stanford.edu/ roozbeh/pascal-context/

COCO: https://cocodataset.org/#home

License: Creative Commons Attribution 4.0 License

Cityscapes: https://www.cityscapes-dataset.com/

License: This dataset is made freely available to academic and non-academic entities for noncommercial purposes such as academic research, teaching, scientific publications, or personal experimentation.

ADE20K: https://groups.csail.mit.edu/vision/datasets/ADE20K/

License: Creative Commons BSD-3 License

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: Our experiments reflect the scope of the paper and the claims made in the abstract and in the introduction.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

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It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

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Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

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Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

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Justification: We detail in section Sec. 4 the datasets splits used to evaluate the method as well as the value of default hyper-parameters.

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The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment Statistical Significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [NA]

Justification: No error bars are reported as standard validation sets are used to evaluate the method.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

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If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

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Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

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Justification: We mention in Sec. 4 the type of graphic card used to run the experiments.

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The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: Our method which reuses pre-trained models in a zero-shot fashion can save computational ressources as well as reduce energy consumption and human labor, contributing to more sustainable AI practices.

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The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

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If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

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Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

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Justification: We do not deem that this paper poses such risk.

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The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a model for zero-shot semantic image segmentation based on clustering\nfeeatures from an off-the-shelf text-to-image diffusion model. Features are extracted\nfrom the U-Net used in the diffusion model. The features are then clustered using\na recursive normalized cut algorithm. The clustering is scaled up to the original\nimage resolution by semantic guidance from the extracted features. The proposed model\nconsistently outperforms previous methods on standard benchmarks for image segmentation.\n\nSTRENGTHS:\n- The paper makes a very targeted contribution to the field. An off-the-shelf feature\n  extractor is combined with an established clustering algorithm and a novel upscaling\n  method. The method is evaluated on standard benchmarks which allows for a clear\n  comparison to prior work.\n- The model is compared to a range of state-of-the-art methods and outperforms them\n  consistently, in most cases by a substantial margin.\n- Several ablation studies are conducted to show the impact of the contributions in\n  comparison to other diffusion model based segmentation methods.\n\nWEAKNESSES:\n- The main contribution explitly states the improvements over the TokenCut and MaskCut\n  models, but does not compare to these models in the experiments.\n- In my view, the comparison of the respective strengths and weaknesses of different\n  base models is not detailed enough. The experiment on semantic coherence targets this\n  direction. However, beyond this analysis it would have been interesting to directly\n  compare the segmentation results after clustering that are obtained from the different\n  base models, both in terms of quantiative metrics and example cases and further\n  analyses that show the differences.\n\nQUESTIONS:\n- The paper should state more clearly that it target *semantic segmention* to avoid\n  confusion with other types of image segmentation. In my view this would have a\n  major impact on the clarity of the paper.\n- How large is the computational improvement over previous methods? The differences are\n  mentioned in terms of parameter count, but a comparison in terms of runtime would be\n  interesting as well.\n- In comparison to standard semantic segmentation, the method proposed here predicts\n  unlabelled classes. Are the features consistent enough to allow for an easy (e.g.\n  linear) mapping to the labelled classes across images?\n\nLIMITATIONS:\nI don't fully agree with the statement that the method does not have potential societal\nimpacts. For example, as briefly mentioned by the authors elsewhere, resusing pre-trained\nmodels in a zero-shot fashion can save computational and labeling resources. In terms\nof energy consumption and human labour, this line of works might have a societal impact.\n\nOne limitation in my view stems from the fact that the diffusion model used as a basis\nprobably does not work well on all types of images. While unsupervised, the approach\nfollowed in the paper is dependent on the capabilities of the foundation model. This is\nnot mentioned in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes a new strategy for the task of unsupervised zero-shot segmentation using diffusion model features. The semantic maps are extracted from the U-net features of a diffusion model by applying a recursive algorithm that allows various levels of granularities of the segmentation maps. The proposed method beats competing methods on unsupervised and open-vocabular semantic segmentation on six datasets.\n\nSTRENGTHS:\nIn general, the work addresses a problem of unsupervised zero-shot segmentation that has not been addressed a lot by the community, proposing new ways to extract knowledge from the rich U-Net features of a diffusion model. While the method is not overly complex and mainly builds on previously published works, it nicely connects more conventional non-DL research from the past with the opportunities arising in foundational models, and it adds a few more technical contributions that are demonstrated to improve performance. In general, the method seems to clearly outperform previous methods on various benchmarks, including the open-vocabulary setting.\nThe paper is clearly written, and the structure allows to easily follow the work’s main components and contributions.\n\nWEAKNESSES:\nMy main concern is that the experimental comparison seems rather unfair. While the proposed method uses powerful distilled SD-XL model the baselines only use SD1.4, which might explain most of the performance improvements. In my opinion, the ablation with respect to SD1.4 is not comprehensive enough: 1) The proposed method is not applied with SD1.4, which I think would be mandatory. 2) The DiffSeg results in Tab. 2 seem to be worse than in Tab. 1. I would appreciate a clarification of the effect of using SD-XL features. The proposed method should also be evaluated using the less powerful SD1.4 features to allow a direct comparison with the original DiffSeg method. Otherwise, the comparisons cannot be really fair, in my opinion.\n\nTab. 4: I assume, DiffSeg would again underperform DiffCut. However, having DiffSeg in this table would be a more complete comparison, not only relaying on the Hungarian algorithm-based evaluation strategy in Tab. 1.\n\nWhen considering Fig. 9, the masks do get more fine-granular for higher tau. However, the masks do not always align with the object boundaries (e.g. the surfer, the roofs, or the sign). I am wondering whether this is because of using the low-dimensional resolution and only bi-linearly upsampling?\n\nWhile the previous method DiffSeg also pursued this strategy, I am unsure if the application of the Hungarian matching algorithm to assign predicted masks to a ground truth mask is fair when comparing with methods that do not apply this strategy for the evaluation. It appears not directly comparable. I would appreciate a discussion of this fact and an argumentation why the presented evaluation strategy is reasonable and comparable to the other methods (that do not apply the Hungarian algorithm).\n\nI like the additional baseline AutoSC, which is also seems to be very effective. While the recursive and adaptive formulation of the baseline seem so to be reasonable, the gap to DiffCut is, however, not very clear. The results are almost the same. I would appreciate a more complete ablation of this. The set of chosen exponents seems to be arbitrary (l.302). Did you study whether AutoSC outperforms DiffCut if more possible alpha values were chosen? I would appreciate such an analysis and discussion of it in the rebuttal. Also, I am wondering – which model is faster, AutoSC or DiffCut?\n\nQUESTIONS:\nReCO is first listed training-free, in the open-vocabulary setting with extra-training, but this is not explained in the text?\n\nRegarding terminology: The section 4.4) model analysis is nicely to read. I would maybe consider replacing the term robustness with hyperparameter sensitivity (also in G). But I have no strong opinion on this.\n\nTab. 3: Did you also study the results if only 64x64 resolution were used?\n\nl.339: Could you please discuss this strategy more comprehensively in the final manuscript (or refer to a related work if existing)? It is not very clear.\n\nLIMITATIONS:\nThe method seems to be profound. However, some important evaluations are missing, as explained above.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses harnessing semantic localization information in pretrained diffusion UNet models. With the proposed recursive normalized cut on the final self-attention features of the diffusion UNet encoder, this paper achieves better performance on unsupervised zero-shot segmentation comparing to previous methods utilizing other fundation vision encoders, with regulation to the granularity of details. Experiments on several benchmarks and ablation studies show the efficacy of the proposed method.\n\nSTRENGTHS:\nThis paper addresses an interesting and important problem of analyzing semantic information in pretrained generative diffusion models comparing to other fundation models. Conducting semantic clusterting on the latents of UNet encoder to reduce computation cost and then applying concept assignment while upsampling seems reasonable. The experiments are overall comprehensive and informative. The emperical results significantly outperform pervious baseline method.\n\nWEAKNESSES:\nMy main concerns consists of several aspects:\n1. The writing seems a bit too vague, maybe with more visual illustraition of the observations? For example, can you demonstrate the \"patch-level alignment\" with visual examples? It's hard to imagine when reading this in Line 168.\n2. Are there any visual verification and description for what kind of concepts you get in Section 3.3?\n3. The experiments with the proposed DiffCut seems to mainly excute with SSD-1B. What about using other SD models? Regarding computation cost and performance benefit.\n\nQUESTIONS:\n1. In Line 223, what do you mean by \"perform a many-to-one matching\"?\n2. Are the hyperparameters the same for DiffCut on different datasets? Since you seem to use the same hyperparameters for DiffSeg?\n\nLIMITATIONS:\nThis author did not discuss the limitations in the paper, which potentially could include:\n1. How is the computation efficiency of the proposed method comparing to other methods? Considering the recursive partitioning and concept assigning process.\n2. The semantic clustering knowledge in pretrained diffusion models might not be able to transfer to data domains like biomedical images.\n3. If LD, AX, or UA is given, would the proposed method still outperform the corresponding SOTAs? Or can it further improve its performance?", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces an innovative unsupervised, zero-shot image segmentation method called DiffCut. This method leverages the encoder features of a pre-trained diffusion model within a recursive graph partitioning algorithm to create finely detailed segmentation maps without requiring labels from downstream segmentation datasets. DiffCut exploits features from the last self-attention block of a diffusion UNet encoder to perform image segmentation. This approach does not rely on paired image and text data, making it suitable for unsupervised and zero-shot learning tasks. The core algorithmic innovation in DiffCut is the use of a recursive Normalized Cut (NCut) that allows the model to regulate the granularity of detected objects and consequently adapt the number of segments to the visual content of each image. Compared to existing methods like DiffSeg and other graph-based object localization techniques, DiffCut significantly outperforms in terms of the quality of segmentation maps and alignment with semantic visual concepts. The effectiveness of DiffCut was validated across multiple standard benchmarks with a focus on mIoU scores, where it consistently outperformed the state-of-the-art unsupervised semantic segmentation methods.\n\nSTRENGTHS:\n1. The paper is clearly written and logically structured, making it easy to understand.\n\n2. The authors conducted thorough experiments to demonstrate the effectiveness of DiffCut, which achieves state-of-the-art performance in many downstream tasks.\n\n3. In contrast to earlier works that employ graph-based clustering methods for unsupervised segmentation, such as TokenCut and MaskCut, this method introduces a soft thresholding technique for constructing the affinity matrix. This approach effectively maintains high affinity between highly similar patches while reducing the weights between dissimilar patches to near zero—an improvement that addresses a limitation often neglected in previous studies.\n\nWEAKNESSES:\n1. **[Unsupervised Instance or Panoptic Segmentation Performance]** While the paper positions itself as effective in segmenting more objects within an image, it primarily focuses on semantic segmentation, potentially overlooking the model's capability for instance discrimination. I am interested in how the model performs in unsupervised instance or panoptic segmentation tasks, as these require differentiating individual objects or integrating both semantic and instance segmentation.\n\n2. **[Technical Contribution and Comparison with MaskCut]** MaskCut is actually capable of segmenting multiple objects per image, with the ability to set a large number of masks per image. This raises questions about the novelty and technical contributions of this paper, particularly in the absence of quantitative comparisons with established methods like TokenCut [30] or MaskCut [32].\n\n3. **[Lack of Comparisons with Previous Works]** The paper does not compare its results with some previous state-of-the-art works in unsupervised panoptic and semantic segmentation, such as U2Seg [N1] and EAGLE [N2]. Notably, [N2] also explores the use of prototypes derived from eigenvectors of feature maps. \n\n4. **[Adequacy of Low-Resolution Features]** The use of low-resolution feature maps to produce concept-embeddings may result in missing small-scale objects in the images, which could lead to the exclusion of these small objects during the \"High-Resolution Concept Assignment\" phase. This could limit the model’s effectiveness in capturing medium or small-sized objects, which are crucial for detailed image segmentation. \n\n5. **[Recursive Normalized Cuts or Multi-class Spectral Clustering]** I am curious if the authors have explored to use spectral clustering for multi-entity/object segmentation. The granularity of the segmentation masks can be controlled by using a different cluster numbers. \n\n\n[N1] Niu, Dantong, Xudong Wang, Xinyang Han, Long Lian, Roei Herzig, and Trevor Darrell. \"Unsupervised universal image segmentation.\" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22744-22754. 2024.\n\n[N2] Kim, Chanyoung, Woojung Han, Dayun Ju, and Seong Jae Hwang. \"EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation.\" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3523-3533. 2024.\n\nQUESTIONS:\nPlease check the weakness section.\n\nLIMITATIONS:\nThe authors adequately addressed the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-13"}