Spatial Structure and Selective Text Jointly Facilitate Image Clustering
Authors: Zizheng Jiu
Feijiang Li
Jieting Wang
Yuhua Qian
Lu Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Image clustering
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Image clustering is a fundamental task in visual machine learning. A key research direction in this field is the incorporation of prior knowledge. Recently, such prior knowledge has evolved from internal compactness constraints to external textual guidance. In particular, the introduction of textual modalities through CLIP has demonstrated impressive performance. However, CLIP is designed primarily for image–text alignment and may not be sufficient to capture clustering structures. Moreover, existing approaches often assume that textual features are universally beneficial, overlooking their varying suitability for different datasets. To address these issues, we propose using spatial structure and selective text jointly to facilitate image clustering (SATC). Specifically, we design a graph attention network (GAT)-based encoder to capture relational dependencies among image patches, thereby extracting spatial features to facilitate clustering. In addition, we introduce a textual feature selector that uses the potential clustering compactness of textual features as the selection criterion and adaptively integrates them into the clustering process. Theoretical guidance is provided for this selector. Finally, the cluster assignment is produced through tri-modal mutual distillation. Extensive experiments on 18 benchmark datasets demonstrate the effectiveness of SATC. The experimental results further verify the rationality of the textual feature selector. **Project Page:** 👉 https://zizhjiu.github.io/SATC/

Published as a conference paper at ICLR 2026
SPATIAL STRUCTURE AND SELECTIVE TEXT JOINTLY FACILITATE IMAGE CLUSTERING
Zizheng Jiu 12.3t Feijiang Li Jieting Wang Yuhua Qian Lu Chen 1,2,3
Institute of Big Data Science and Industry, Shanxi University
2 Key Laboratory of Evolutionary Science Intelligence of Shanxi Province, Taiyuan. Shanxi. China School of Artificial Intelligence, Shanxi University ginchenggyhe 8226. com
ABSTRACT
Image clustering a fundamental task in visual machine learning. A key research direction in this field is the incorporation of prior knowledge. Recently, such prior knowledge has evolved from internal compactness constraints to external textual guidance. In particular, the introduction of textual modalities through CLIP has demonstrated impressive performance. However, CLIP is designed primarily for image-text alignment and may not be sufficient to capture clustering structures. Moreover, existing approaches often assume that textual features are universally beneficial, overlooking their varying suitability for different datasets. To address these issues, we propose using spatial structure and selective text jointly to facil- itate image clustering (SATC). Specifically, we design a graph attention network (GATT)basedd encoder to capture relational dependencies among image patches, thereby extracting spatial features facilitate clustering. In addition, we intro- duce a textual feature selector that uses the potential clustering compactness of textual features as the selection criterion and adaptively integrates them into the clustering process. Theoretical guidance is provided for this selector. Finally, the cluster assignment is produced through tri-modal mutual distillation. Extensive experiments 18 benchmark datasets demonstrate the effectiveness of SATC. The experimental results further verify the rationality of the textual feature selec- tor. The project page is available at https://zizhjiu. http://iiiiiu.ggiuuu.ooooooo
INTRODUCTION
Image clustering aims to group unlabeled images into semantically meaningful clusters, playing key role in various real-world applications, such as image retrieval and dataset organization (Huang et al., 2024). A core aspect of image clustering lies in the incorporation and effective use of prior knowledge reveal the underlying data structure. In the absence of explicit labels, prior knowledge provides essential guidance to the clustering process.
Most traditional and deep clustering methods rely on the prior of cluster that samples belonging to the same category naturally cluster together in the feature space. Clas- sical methods, such as K-Means (Krishna Murty, 1999), operate on handcrafted features. These shallow features fail to capture complex visual differences. Deep clustering methods overcome this limitation by jointly learning feature representations and cluster assignments in an end-to-end manner. For instance, DEC (Xie al., 2016) optimizes self-supervised objective refine clus- ters while optimizing feature representations. Subsequent works (Han et al., 2020; Yu et al., 2020) further enhance clustering performance by improving representation quality and cluster discrimi- nation. Despite these advances, such methods remain constrained to internal supervision signals derived solely from the data itself.
These authors contrihute equally this work.
Corresponding author.

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Published as a conference paper at ICLR 2026
Recently, inspired by cross-modal foundation models such as CLIP (Radford et al., 2021), re- searchers have begun explore the use of ad- ditional textual guidance as prior knowledge for image clustering. For instance, TAC (Li et al., 2023) proposes leveraging rich external textual knowledge to guide the clustering process, in- troducing new direction for image clustering. Built upon CLIP, TAC exploits the natural se- mantic alignment between image-text pairs to enhance clustering quality. By enabling cross- modal learning, TAC introduces more informa- tive prior knowledge into the clustering frame- work, illustrating the potential of external tex- tual priors to overcome the limitations inherent in methods that rely solely internal supervi- sion signals.
Class
Namss
Shibu Inu
Shibu Inu
Keeshand
Skila
Kendland
Visual
Similarity
0,7246
Tectal
Similarit
0.5049
0,8633
0.6812
Spotial
Similarily
0.4158
0.2033
0.2048
Figure I: Pairwise cosine similarities among two Shiba Inu images and one Keeshond image from the OxfordPets dataset, evaluated across visual, textual, and spatial modalities. Visual features extracted using CLIP, textual features are obtained following TAC (Li al., 2023), and spatial fea- tures are derived from our proposed SATC.
However, CLIP is primarily trained align im- ages and text within a shared semantic space, which may compromise the representation of spatial structures. The internal compactness of the image is influenced by the spatial structure, which encodes rich information such as an ob- ject's shape, position, and part layout. This in- formation plays key role in downstream clustering performance. As illustrated in Figure 1, when clustering two Shiba Inu images and one Keeshond image, both visual and textual similarities mis- takenly favor aligning Shiba Inu with the Keeshond rather than correctly grouping the two Shiba Inu images. Compared to visual and textual modalities, spatial modalities more effectively capture intra-class similarity and better distinguish between categories.
Moreover, textual features are often assumed to be universally beneficial across different datasets, without assessing their suitability to specific data. In practice, textual features may not always be suitable for all datasets (Zhu et al., 2025a). As illustrated in Figure 1, textual similarities can sometimes fail capture category-level distinctions, and indiscriminately incorporating them may introduce noise, ultimately degrading clustering performance. We evaluated the TAC method across all 18 datasets listed in Table 4 in Appendix and compared its performance against text-free baseline. The results (see Appendix Table 8) demonstrate that textual features do not universally improve clustering performance across all datasets.
To address these challenges, we propose a novel image clustering framework, Spatial Structure and Selective Text Jointly Facilitate Image Clustering (SATC). SATC integrates three key components to improve clustering performance. First, it employs GAT-based spatial encoder to capture spatial relationships among image patches, thereby overcoming the limitations CLIP in capturing local structure. Second, it introduces a textual feature selector that evaluates the potential clustering com- pactness textual features to selectively incorporate the textual feature into the clustering process, reducing the misleading of textual signals. Third, it adopts a tri-modal mutual distillation strategy to improve cluster discrimination by jointly leveraging visual, spatial, and textual modalities.
The main contributions of the paper
follows:
We propose textual feature selector that evaluates and adaptively incorporates textual information based on its potential clustering compactness, which is used to improve clus- tering robustness across diverse datasets. Thcoretical guidance for this selector is provided.
We introduce a spatial feature that explicitly encodes relational structure among image patches and further integrate it with visual features and beneficial textual features through a tri-modal mutual distillation framework, ultimately generating cluster assignments.
We conduct extensive experiments 18 benchmark datasets, demonstrating that our method outperforms state-of-the-an approaches and validating the effectiveness of com- bining spatial features and beneficial textual guidance for image clustering.
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Published as a conference paper at ICLR 2026
2
RELATED WORK
2.1 IMAGE CLUSTERING
Internal Supervision Signal Methods. Most of the existing methods rely on an internal compact- ness constraint to guide the learning of cuusterin--freendee representations. They typically assume that samples from the same category are naturally closer in the feature space and optimize the rep- resentations accordingly. Early works such DEC (Xie al., 2016) refine cluster assignments by minimizing the KL divergence between current predictions and a sharpened target distribution. Methods like IIC (Ji et al., 2019). SCAN (Van Gansbeke al., 2020). and CC (Li al., 2021) lever- age prediction consistency under strong data augmentations to facilitate unsupervised clustering, whereas approaches such as DeepCluster (Caron al., 2018) and SPICE (Niu et al., 2022) itera- tively assign pseudo-labels to supervise feature learning. Graph-based extensions like GATClus- ter (Niu et al., 2020) introduce graph attention mechanisms to model neighborhood dependencies and improve clustering quality.
External Textual Guidance Methods. Recently, some methods rely on external textual prior knowledge to guide the learning of clustering-friente representations. They aim to overcome the limitations internal supervision by introducing cross-modal supervision from the textual modali- ties. SIC (Cai et al., 2023) generates pseudo-labels in the textual feature space, while TAC (Li al., 2023) utilizes textual features generated by CLIP as auxiliary supervision to enhance clustering per- formance. These methods highlight the potential of external textual guidance. However, they often rely on the assumption that the textual features are consistently reliable.
2.2 CLIP- REPRESENTATION LEARNING
Contrastive Language- Image Pretraining (CLIP) (Radford et al., 2021) has emerged as a powerful foundation model by aligning visual and textual features through large-scale contrastive learning- An increasing number of studies have leveraged CLIP across various downstream tasks. For exam- ple, WeakCLiP (Zhu et al., 2025b) adapts CL.IP weakly supervised semantic segmentation. the context of few-shot learning. MORN (Ni al., 2024) utilizes CLIP to generate modality-specific prototypes and integrates them via a cross-moda enhancement module to improve action recog- nition. Similarly, CLIP-RPN (Guan & Yoshie, 2025) exploits CLIP's semantic perception to adap- tively route features for image deraining based on rain-pattern awareness. These diverse applications demonstrate CLIP's versatility in leveraging cross- modal representations. However, although CLIP excels at image-text alignment, it lacks explicit mechanisms for capturing relational dependencies among image patches-a limitation that constrains its effectiveness for image clustering tasks.
To address the limitations, we propose GAT-based spatial encoder that explicitly captures relational dependencies among image patches, alongside textual feature selector that assesses and adaptively integrates textual modality across different datasets. Building on these components, we develop SATC a robust and adaptive image clustering framework that effectively leverages both spatial structures and textual priors.
THE PROPOSED METHOD
In this section, we describe the proposed SATC, illustrated in Figure SATC is designed over- come the limitation of CLIP while mitigating the impact of unreliable textual feature guidance by incorporating (a) visual and spatial feature extraction, (b) compactness- textual feature selec- tion, and (c) tri-modal mutual distillation.
3.1 VISUAL AND SPATIAL FEATURE EXTRACTION
For the raw image dataset D (x 1.N features 8 visuul E Rd for images in dataset D, where d is the feature dimension predefined by the CLIP model.
leverage the pretrained CLIP model to extract visual
Motivated by the work of (Qian et al., 2015), which introduces a space-structure--ee sstructure-based represen- tation to improve clustering for categorical data, we explore extracting spatial structure features to
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Textual Encoder
distill
Shupherd
Textual
Thresbold
coniaa
Farrppy
Testmal Feature Selection
K-Meass
I.. Tri-Modal
Layert
Claster
Layer3
Laver2
GAT
Layerl
Spatial Fennures
Ciuan Cicurie
Larti D
Mean-Pool
Cluster Assignment
Keha
placial eartopyy
Visual Encuder
Conv2d
Visuil Features
Spatial Cluster
Visual
Cluster Head
Head
I. Viuual and Spatial I cotures Extruction
Figure 2: Overview of the SATC, The pseudocode is presented in Appendix H Algorithm
facilitate improved image clustering performance. To this end, apply Graph Attention Net- work (GAT) (Veličković et al., 2017) to patches within each individual image to capture spatial relationships among different regions of the same image. GAT is neural network designed for graph-structured data that updates each node by attending to its neighbors using learnable attention weights. The attention mechanism in GAT enables adaptive weighting of neighboring patches, al- lowing it to effectively capture relationships among image patches. Specifically, for each image, we first divide into patches and extract patch-level feature using a pretrained ResNet-50 (Radford et al., 2021) with the final pooling and fully connected layers removed. Each patch feature serves as a graph node, and edges are constructed by connecting semantically related patches within the same image. The resulting node features are denoted as X [r],... FM E M where M is the number patches in the image, and the edge set E (i. J. i,j (M....,MM) M based on feature similarity.
Then, we feed the node features along with the edge set E into GAT, which updates each node by aggregating information from its neighbors with attention. The updated representation x' for node i can be expressed as
exp(f(x;; xT,)) 3g
x/ =
Σ (5)
ENENTO) exp( exp([(1x)) mx))
where (ii) denotes the set of neighbors of node i, ((.) is a nonlinear activation function, and f .r)) is a learnable function that computes the attention score between nodes and GAT.
After obtaining the refined node features X' [ LM Mxd we apply global average pooling over the M nodes, obtaining the spatial embeddings z-putial Rd The embeddings are then used as an additional input to the downstream clustering process.
3.2
COMPACTNESS-AWA.. TEXTUAL FEATURE SELECTION
3.2.1 TEXTUAL FEATURE EXTRACTION
To generate textual features for clustering, adopt the text counterpart construction process in- troduced in TAC (Li et al., 2023). Specifically, we first collect candidate nouns T5 E Rdd from WordNet (Miller, 1995) using CLIP.
To align textual features with the visual features, we first cluster the visual features z visual into K1 = [N/300] clusters by K-Means. The resulting cluster centers are denoted {4](F] To ensure that each semantic center represented by highly discriminative textual concepts, identify the top-
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Published as a conference paper at ICLR 2026
5 confident nouns {Tm 1 m= for each cluster. These nouns that exhibit the highest posterior probabilities of belonging to the corresponding cluster center. Formally, the posterior probability of assigning noun I3 to a cluster is computed as:
e(sim(T3.14))
ply =1TT)
(2)
K1 i=1 e(sim(TT..44))
where sim(.,-) denotes the cosine similarity.
The textual feature z! z/extual corresponding to the visual feature 2, 2ل (for sample (I,...,NN) is computed via soft retrieval over Tm:
5 ((iim( thaa .f)/9t)
textual
j=1 Ex=1 e(sim(=)" tooe .T)/B1) TT.
(3)
where 81 = 0.005 is temperature hyperparameter.
3.2.2 TEXTUAL FEATURE SELECTOR
To leverage the textual modality that beneficial for image clustering, we introduce textual feature selector based on textual compactness. Inspired by the concept of intra-cluster compactness in internal supervision signal clustering methods. Theoretically, if features belonging to the same class are highly concentrated in the vector space, they are more discriminative and can effectively distinguish different categories. For the textual modality, a lower compactness score indicates that textual features highly clustered and may be semantically redundant, whereas higher score reflects greater semantic diversity, suggesting that the textual modality can provide more valuable guidance for clustering. This motivates the of textual compactness
criterion.
We first generate the textual features and assess their compactness 7 by clustering the pre-extracted textual embeddings zlextual into K clusters using K-Means, where K denotes the number of classes in the object dataset. For the j-th cluster, the assigned textual samples form the set D,, and the cluster center C₁ is computed as:
1 Dj
C2
textnal 3j
(4)
MOII ED
Based on the Cj. the textual compactness metric is defined as the average intra-cluster distance between the textual features and their corresponding cluster centers:
1| rextunl
C
(5)
K
D2
ED
The textual compactness 7 is used as dataset-level prior. Before training the tri-modal distillation framework, we compute 7 on the entire dataset's textual feature. If r is higher than a threshold, consider the textual modality to be beneficial and include it in the mutual distillation process; otherwise, we exclude it. In Appendix I., a theoretical guidance for utilizing textual compactness is given. This adaptive selection ensures that the textual modality contributes positively only when it provides sufficiently diverse and discriminative information. The effectiveness of this textual compactness-baatt -based selection strategy is validated through extensive experiments in Section 4.3.1.
3.3 TRI-MODAL MUTUAL DISTILLATION
To effectively integrate tri-modal information for image clustering, we propose a tri-modal distil- lation framework. This framework takes pre-trained visual (2 visuul,),, spatial (==palia)) and textual (zletual, used only when > threshold) features as input. Each modality feature is projected into a cluster assignment distribution via a dedicated MLP cluster head. Following mutual distillation, spatial and textual feature serve auxiliary information in the image clustering task, and therefore we take the cluster assignment distribution from the distilled visual modality cluster head as the final assignment. A detailed comparison of different modality cluster heads is provided in Appendix K.

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Published as a conference paper at ICLR 2026
The total training objective integrates all components with balancing hyperparameters A1 and Ag:
3
Cke distill
+ A1
comsist
A2
m entropy"
(6)
MEM
where M {visual, textual, spatial]. There are two cross-modal losses, which are distillation loss and consistency loss, and one internal loss that is evaluated by entropy loss. The three loss functions act synergistically. enabling the model to achieve better performance. A detailed analysis of each loss function, both individually and in combination, is provided in Appendix E. Specifically, the losses are:
distiin C the distillation and consistency losses between visual and spatial modalities; the distillation consistency losses between textual and spatial modalities; cossiis the distillation and consistency losses between visual and textual modalities;
distill Leonsisi
C3
C3
distill>
Cvisual
eapatill entropy Cnnrrop'' the entropy loss of the visual, textual, and spatial modalities.
entropy
To facilitate understanding, we detail the computation of Caisiill Coossiar and Cyisual as representative examples, the remaining losses are computed analogously.
Mutual Distillation Loss. Each loss encourages the cluster assignment distributions of two modal- ities to aligned. Taking the visual and spatial modalities for example, c; caisual and c; spatial denote the soft cluster assignments of the i-th sample from the two modalities, the distillation loss is:
N
distill
Σ
(CS-V
S-V
+ C. +)
(7)
N
1=1
exp [sim(c (ittu ci (ppatal) 1/T)
log
(8)
ΣS-1 exp
(i Grisual epputial) )/T)
exp (sim(c?pain
Spatial c //T)
CV- - -log
(9)
(ppptaal Σラ= exp sim(c
cyisual) /T)
where T' a temperature hyperparameter.
Consistency Loss. To further enhance alignment between modalities, we introduce consistency loss that encourages similar cluster assignments between each anchor sample and its other modality. Taking the visual and spatial modalities for example, the loss is computed as:
N
C
1 N
log ((giiual) T spatial
(10)
Entropy Loss. To prevent degenerate solutions and encourage confident cluster assignments, compute the entropy each sample's s cluster assignment distribution and take the average over all samples. The entropy loss for the visual modality is defined as:
K visual log c.j
evisual cutropy
visual
(11)
1=1
j=1
where visual denotes the soft assignment probability of the i-th sample the j-th cluster in visual modality and K is the number of clusters.
4 EXPERIMENTAL ANALYSIS
In this section, we conduct comparative experiments and ablation studies to investigate the effec- tiveness and robustness of SATC.

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Published as a conference paper at ICLR 2026
4.1 EXPERIMENTAL SETTINGS
Datasets and Evaluation Metric. The experimental analyses are conducted on 18 diverse bench- mark datasets. Detailed descriptions about these datasets are provided in Appendix A. All experi- ments use the original train-test splits. To assess performance, we employ three standard metrics, which are Accuracy (ACC), Normalized Mutual Information (NMI) (Estévez et al., 2009), and Ad- justed Rand Index (ARI) (Steinley, 2004), where higher values indicate better clustering perfor- mance. To ensure statistical reliability, we report the mean results over ten independent runs with different random seeds.
Implementation Details. We use the CLIP ViT-B/32 model (Dosovitskiy et al., 2020) for visual and textual feature extraction. We process WordNet nouns (Miller, 1995) using CLIP to generate candidate nouns. clustering, we adopt three modality-speciffe MLP cluster heads (512-512-K) The model is trained for 200 epochs with batch size of 512. We introduce early stopping mechanism: if the ACC does not improve over 10 iterations, the distillation process is stopped. According to TAC (Li et al., 2023), we set the temperature for mutual distillation to T 0.5. Based on extensive experiments, we set the weights for the consistency and entropy losses to b = 1.0 and X2 = 5.0, respectively (Section 4.4), and the compactness threshold to 0.33 (Section 4.3.1).
4.2 COMPARISON EXPERIMENT
We evaluate SATC on 5 widely used datasets, including ImageNet-10, 10, ImageNet-Dogs STL-10, CIFAR-10, 10, and CIFAR-100 To assess its effectiveness, we compare it with a wide range of classi- cal and state-of-the-ar clustering methods, including K-Means (Krishna & Murty, 1999), DEC (Xie al., 2016), DAC (Chang et al., 2017), DCCM (Wu et al., 2019), DSEC (Chang et al., 2018), GATCluster (Niu et al., 2020), PLCA (Huang al., 2020). CC (Li al., 2021), C3 (Sadeghi al., 2022), MICE (Tsai et al., 2020), IDFD (Tao et al., 2021), TCL (Li et al., 2022), CONCUR (Desh- mukh et al., 2022), SPICE (Niu et al., 2022), DPAC (Yan et al., 2024), TAC (Li et al., 2023), DINOv2-VitB/14 (Oquab et al., 2023) + K-Means, DINOv3-VitB/L (Siméoni et al., 2025) + K- Means, Turtle (Gadetsky al., 2024), GradNorm (Peng al., 2025), and LESS (Li et al., 2025).
Table I: Clustering performance of different approaches evaluated by Accuracy (ACC%), Normal- ized Mutual Information (NMI%). and Adjusted Rand Index (ARI%). indicates unavailable results. The best and second- best results are highlighted bold and underline, respectively.
ImageNet-10 10
ImageNet-Dogg
STL-10
CIFAR- 10
CIFAR- 100
Method
ACC
NMI ARI
ACC
NMI
ARI
ACC
NMI
ARI
ACC
NMI
ARI
ACC
NMI
ARI
K-Means
24.1
11.9
5.7
10.5
5.5
2.0
19.2
12.5
6.1
22.9
8.7
4.9
13.0
8,4
2.8
DEC
38.1
28.2
20.3
19.5
12.2
7.9
35.9
27.6
18.6
30.1
25.0
16.1
18.5
13.6
12.5
DAC
52.7
39.4
30.2
27.5
21.9
11.1
47.0
36.6
25.7
52.2
40.0
30.1
23.8
18.5
8.8
DCCM
71.0
60.8
55.5
38.3
32.1
18.2
48.2
37.6
26.2
62.3
49.6
40.8
32.7
28.5
17.3
DSEC
67.4
58.3
52.2
26.4
23.6
12.4
48.2
40.3
28.6
47.8
43.8
34.0
25.5
21.2
11.0
GATCluster
76.2
60.9
57.2
33.3
32.2
20.0
58.3
44.6
36.3
61.0
47.5
40.2
28.1
21.5
11.6
PICA
87.0
80.2
76.1
35.2
35.2
20.1
71.3
61.1
53.1
69.6
59.1
51.2
33.7
31.0
17.1
CC
89.3 85.9
82.2
42.9
44.5
27.4
85.0
76.4
72.6
79.0
70.5
63.7
42.9
43.1
26.6
C3
94.2 90.5
86.1
43.4
44.8
28.0
83.8
74.8
70.7
45.1
43.4
MiCE
43.9
42.3
28.6
75.2
63.5
57.5
83.5
73.7
69.8
44.0
43.6
28.0
IDFD
95.4
89.8
90.1
59.1
54.6
41.3
75.6
64.3
57.5
81.5
71.1
66.3
42.5
42.6
26.4
TCL
89.5
87.5
83.7
64.4
62.3
51.6
86.8
79.9
75.7
88.7
81.9
78.0
53.1
52.9
35.7
ConCUR
90.7
90.9
63.0
53.1
74.9
56.6
84.6
76.2
47.9
46.8
SPICE
96.9
92.7
93.3
67.5
62.7
52.6
92.9
86.0
86.5
91.8
85.0
83.6
58.4
58.3
42.2
DPAC
97.0 92.5
93.5
72.6
66.7
59.8
93.4
86.3
86.1
93.4
87.0
86.6
55.5
54.2
39.3
DINOv2
96.3 93.7
91.6
87.5
88.1
80.4
77.4
81.5
64.0
77.0
81.6
64.5
62.5
69.8
46.1
DINOv3
96.7 93.8
92.5
48.3
48.2
27.3
98.1
96.0
96.0
90.1
85.0
81.1
57.6
70.3
42.6
Turtle
98.3
98.6
49.6
44.6
32.4
98.4
95.5
96.1
86.6
78.8
75.4
46.4
58.7
34.5
GradNorm
99.4
98.7
98.7
81.2
81.0
70.9
98.3
95.6
96.2
91.1
82.6
81.5
LFSS
93.2
85,6
85.7
69.1
61.7
53.3
86.1
77.1
74.0
93.4
87.2
86.6
TAC
99.5
98.5
98.8
84.4
77.4
72.0
98.3
95.7
96.3
92.2
83.7
83.6
59.0
68.6
42.7
SATC
99.8
99.2
99.4
91.4
89.7
86.7
99.0
97.3
97.9
94.5
88.9
88.3
63.4
70.5
48.1

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Published as a conference paper at ICLR 2026
The results are summarized in Table 1. From the table, it is evident that SATC consistently out- performs other approaches across all datasets. Specifically, SATC achieves the highest accuracy, attaining accuracy of 99.8% ImageNet-10, with improvements over the second-best method (TAC) of 7.0% on ImageNet-Dogs and 4.4% on CIFAR-100, among others. In terms of NMI and ARI, SATC also leads, with substantial gains competing methods. These results highlight that the SATC effectively improves the clustering performance through integrating spatial structural in- formation and selectively incorporates textual features in a synergistic manner to facilitate image clustering. Moreover, the consistent gains across datasets demonstrate that SATC is robust and generalizable, effectively handling complex intra-class and inter-class variations while enhancing clustering accuracy and stabilizing the learning process.
4.3
ABLATION STUDIES
4.3.1 ANALYSIS ABOUT TEXTUAL FEATURE SELECTOR
To validate the effectiveness of our compactness-aware textual feature se- lector, we evaluate clustering performance with and without leveraging the textual modality across 18 benchmark datasets.
Table 2: The results of the textual feature selector, including compactness т, the clustering ACC with and without textual features, and the indicator of the textual feature selector. Bold indicates the best ACC under the Use text No text. Gray Shading de- notes the result with the proposed textual feature lector. Dataset indices refer to Appendix A.
As described in Section 3.2.2, the compact- ness measures the intra-cluster concen- tration of textual features: lower т indi- cates that textual features are highly clustered and potentially semantically redundant, while higher suggests greater semantic diversity, making the textual modality more informa- tive for clustering.
Dataset
T
No Text
Use Text
T > 0.33
1
0,1083
89.8
60.7
X
2
0.1241
85.8
59.9
X
3
0.1476
99.8
99.6
X
4
0.1541
36.2
34.5
X
5
0.1787
76.0
74.0
X
From Table 2, we observe a clear correla-
6
0.1892
31.5
26.1
X
tion between the compactness т and the use- fulness of textual information: datasets with
7
0.2027
86.3
83.4
X
8
0.2247
91.4
87.0
X
lower T values (T 0.33) generally show
9
0.3084
81.4
72.2
minimal or even negative benefits from incor- porating textual features. while datasets with higher values (r > 0.33) tend to benefit significantly. According to the experimental results, the selection of textual compactness threshold 0.33 obtains the optimal results on the 18 datasets. We would like clarify that 0.33 could be understood as an empirically discovered rule derived from broad bench- marking, rather than hyperparameter tuned on test performance. This value was identified based on aggregated trends observed across all 18 datasets in our study. Importantly, it was not iteratively optimized for any specific dataset's 's test performance.
0,3222
89.4
84.9
11
0,3226
52.3
49.6
X
0.3349
93.6
13
0,3623
60.6
67.1
14
0,3780
98.5
99.0
15
0.3839
52.2
65.2
16
0.4155
56.7
63.4
17
0,4361
51.6
60.5
18
0.4822
47.4
52.0
The results show that the proposed textual feature selector exactly matches the highest ACC values under both the "Use Text" and "No Text" settings for each dataset, demonstrating that by leverag- ing the textual compactness т, SATC can adaptively select textual features that are beneficial for downstream clustering.
4.3.2 ANALYSIS OF SPATIAL MODELING ARCHITECTURES
In our framework, spatial feature extraction involves two key components: ResNet-50 and GAT. ResNet-50 first divides the input image into patches and generates node features for each patch, providing rich local spatial representations. The GAT then models relational dependencies among these patches. To evaluate their individual contributions, we conducted ablation studies comparing: (1) using only ResNet-50 features, (2) replacing GAT with GCN or Transformer.
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Table presents ablation study spa- tial components. The first column provides baseline results: the upper section shows K- Means applied to raw CLIP feature, while the lower section incorporates textual feature through mutual distillation. In the second col- umn, we introduce the ResNet-50 node fea- ture. with the lower section further including the textual feature. Columns 3-5 compare different spatial modeling choices.
Table Comparison of spatial modeling (ACC%).
CLIP
/
ResNet-50
/
Choice
NONE
NONE
GCN
TRANS
GAT
56.5
75.2
89.7
76.2
89.8
78.2
76.1
78.4
85.8
99.6
99.8
99.7
99.8
31.6
34.7
38.2
34.7
36.2
5
64.2
72.5
74.3
72.5
76.0
6
27.0
30.7
31.4
31.2
31.5
Comparing the first two columns, we observe that ResNet-50 node features improve clus- tering accuracy over the raw CLIP feature. In addition, results show that spatial model- ing consistently improves performance, with
7
64.3
81.6
85.8
89.5
86.3
8
38.9
84.1
86.7
84.5
91.4
9
67.4
78.3
78.7
73.2
81.4
10
52.4
75.1
86.0
87.6
89.4
11
46.5
48.5
50.2
49.5
57.4
12
92.2
94.0
94.2
94.0
94.5
GAT achieving the highest accuracy GCN and Transformer. GAT's advantage may from its adaptive attention mech- anism, which dynamically weights neighbor- ing patches rather than using uniform aggre- gation (GCN) or fixed receptive fields (Trans- former).
13
67.6
67.2
69.7
69.6
67.1
14
98.3
98.7
98.9
98.7
99.0
15
62.9
61.4
65.0
64.4
65,2
16
39,0
59.3
59.3
56.5
63.4
17
58.6
55.3
58.9
54.7
60.5
18
46.6
49.3
$1.2
52.3
52.0
Avg.
60.8
69.1
71.9
70.4
73.7
The detailed analysis of whether performance improvement comes from the method design or from the use of more powerful pre-tee trainad features given in the Appendix M.
4.3.3
ANALYSIS ABOUT THE SPATIAL FEATURE
100
100
100
80
80
80
60
40
40
+V
+5
+V+S
+V
+S
+V+S
+V
+5
+V+S
(a) ACC
(b) NMI
(c) ARI
Figure 3: Average clustering performance across 18 datasets using visual features via K-Means
(+V), spatial features via K-Means (+S), and their combination via mutual distillation (+V+S).
To verify the effectiveness of spatial features in visual representation, we applied K-Means clus- tering types of features: visual features extracted by CLIP (+V) and spatial features alone (+S), In addition, we performed clustering on a combination of visual and spatial features using mutual distillation (+V+S) implemented in our SATC framework. Figure shows the average performance across 18 datasets, with the detailed results for each dataset provided in Appendix G.
From Figure 3, it can be observed that clustering on visual features (+V) and spatial features alone (+S) achieves comparable overall performance across the 18 datasets, indicating that both modalities capture valuable but distinct information. When the two are combined through mutual distillation (+V+S), the average clustering results surpass those of using single modality. This suggests that visual and spatial features provide complementary signals: the spatial modality offers structural cues that enhance the discriminative power of the visual modality, while the visual modality enriches the semantic meaning of the spatial patterns. These results show that combining visual and spatial features improves clustering quality and robustness.

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Published as a conference paper at ICLR 2026
4.4 PARAMETER ANALYSIS
To investigate the influence of the balaneing hy- perparameters Å and A2 in our training ob- jective (Eq. 6), we conduct parameter analy- sis experiments on CIFAR-10 and ImageNet- 10. Here, À controls the weight of the con- sistency loss and 2 regulates the entropy loss.
1300
800
ACC/P9
300
Figure shows that SATC maintains stable performance across wide range of λ and Ag. Especially on ImageNet-10, 10, the model not only highly robust but also consistently achieves over 99.6% accuracy.
X1 15
A1
(a) CIFAR-10
(b) ImageNet-10 10
Figure Parameter analysis of X and X2 on CIFAR-10 and ImageNet-10. The 3D bar charts show clustering ACC (%) under different hyper- parameter combinations.
These results indicate that A1 and X2 play complementary roles: Å encourages agree- ment across modalities, while A2 prevents over- confident unimodal predictions.
4.5
TIME ANALYSIS
All experiments are conducted on a single Nvidia RTX 4060 Ti GPU. Figure presents a detailed comparison of running time and accuracy between SATC and TAC across 18 datasets. Overall, SATC achieves higher clus- tering accuracy and lower computation time compared with TAC most of the datasets, highlighting the efficiency and effectiveness of SATC. The ACC and running time for each dataset are provided in Appendix C.
100
TAC SATC
STC50
CFAR-12 NUST
Imagetes Poga
Imagh/uus DeggDogs
80
RessoRS
ACC (9)
CIPAN
Taxc on
60-
Floeaza
4.6 TOP-30 DISCRIMINATIVE NOUNS FOR FOUR REPRESENTATIVE DATASETS
40-
CYSRB
PERZULS
In Appendix I, list the top-30 discrimina- tive nouns and word cloud visualizations for two datasets where textual features are uti- lized (CIFAR- 10. STL-10) and two where they excluded (MNIST, FER2013). The nouns show that for datasets where textual features are harmful, the nouns are largely unrelated to the visual content. In contrast, datasets where text is beneficial exhibit semantically relevant terms. The word cloud visualizations clearly demonstrate that higher r datasets exhibit richer lexical diversity and semantic relevance, while lower 7 datasets display limited.
Time (s. log scale)
Figure Comparison of ACC and Times for SATC and TAC across 18 datasets.
CONCLUSION
In this work, we propose an image clustering approach that utilizes spatial structure and selective text to jointly facilitate image clustering (SATC). SATC employs GAT' to effectively model spa- tial relationships among image patches while adaptively integrating textual features through a novel textual feature selector. The framework further enhances performance through tri-modal mutual distillation framework that optimally fuses visual, spatial, and textual modalities. Extensive experi- mental results demonstrate the effectiveness of the proposed SATC and the rationality of the textual feature selector. Future research directions include integrating more diverse prior knowledge and developing advanced fusion strategies enhance performance.
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ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (Nos. 62476160, 62441239, T2495251, 62136005, 02306170, U24A20253), the Special Fund for Science and Tech- nology Innovation Teams of Shanxi Province (No. 202304051001001).
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APPENDIX
The appendix is organized as follows:
A Datasets
B Clustering Performance Comparison
C Running Time Results
D Visualization Analysis
E Loss Function Analysis
F Distillation Direction Analysis
G Spatial Feature Analysis
H SATC Algorithm Framework
Top-30 Discriminative Nouns for Four Representative Datasets
Comparison of Visual and Visual+ Clustering Performance
K Comparison of Different Modality Cluster Heads
L Theoretical Guidance for Utilizing Textual Compactness
The Contribution of ResNet-50 to Model Performance
A DATASETS
We evaluate our framework 18 widely used benchmark datasets, covering diverse range of vi- sion tasks. These include general object classification datasets CIFAR-10 10 (DeVries & Taylor, 2017), CIFAR-101 100 (DeVries & Taylor, 2017). STL-10 (Coates al.. 2011), TinylmageNet (Le Yang, 2015), and ImageNet-10 (Chang et al., 2017); fine-grained object classification datasets Food101 (Bossard et al., 2014), Flowers (Nilshack Zisserman, 2008), Flowers(Test) (Nilshack Zisser- man, 2008), ImageNet-Dege (Chang al., 2017). and OxfordPets (Parkhi et al., 2012); handwritten digit datasets MNIST (Schott et al., 2018) and USPS (Samkaranarayanan et al., 2018); the fashion dataset Fashion-MNIST (Xiao et al., 2017); the texture dataset DTD (Cimpoi et al., 2014); the facial emotion recognition dataset FER2013 (Goodfellow et al., 2013); the satellite image classification datasets EuroSAT (Helber al., 2019) and RESISC45 (Cheng et al., 2017); and the German Traf- fic Sign Recognition Benchmark (GTSRB) (Stallkamp et al., 2012). The detailed statistics of each dataset are summarized in Table 4.
Table 4: Summary statistics of datasets, including number classes, train size, and test size.
Dataset
Num Classes
Train Size
Test Size
MNIST (Schott al., 2018)
10
60,000
10,000
USPS (Sankaranarayanan al., 2018)
7,291
2,007
ImageNet- (Chang et al., 2017)
10,500
2,630
GTSRB (Stallkamp et al., 2012)
43
26,640
12,630
Fashion (Xiao al., 2017)
10
60,000
10,000
FER2013 (Goodfellow al., 2013)
7
28,709
7,178
EuroSAT (Helber al., 2019)
10,000
5,000
ImageNet- (Chang et al., 2017)
15
15,600
3,900
Resise45 (Cheng et al., 2017)
45
25,200
6,300
10
OxfordPets (Parkhi al., 2012)
37
3,680
3,669
DTD (Cimpoi al., 2014)
47
3,760
1,880
CIFAR-10 (DeVsies Taylor, 2017)
50,000
10,000
13
Flowers(Test) (Nilsback & Zisserman, 2008)
102
6,149
2,040
14
STL-10 (Coates et al., 2011)
10
5,000
8,000
15
Food101 (Bossard et al., 2014)
101
75.750
25,250
16
CIFAR-100 (DeVries Taylor, 2017)
100
50,000
10,000
Flowers (Nilshack Zisserman, 2008)
102
2,040
6,149
18
TinylmageNet (Le & Yang, 2015)
200
100,000
10,000
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B CLUSTERING PERFORMANCE COMPARISON
Table 5 presents a comprehensive comparison clustering performance for ClipKmeans, TAC, and our proposed SATC across 18 widely used benchmark datasets, measured in terms of ACC (%), NMI (%), and ARI (%). Overall, SATC consistently achieves the highest performance on the majority of datasets, with average ACC, NMI, and ARI of 73.7%. 76.3%, and 63.8%, respectively. This demonstrates that SATC effectively exploits both rich spatial features and selectively incorporated textual information, producing clusters that are not only more accurate but also more robust and semantically meaningful, compared with the visual-only ClipKmeans baseline (59.4% / 60.9%. 46.3%) and the text-enhanced TAC method (63. 1% 65.6% 51.3%).
The performance gains of SATC are particularly prominent on datasets with complex visual struc- tures or high intra-class variability. For instance, on MNIST, ACC increases dramatically from 56.5% (ClipKmeans) and 39.8% (TAC) to 89.8% with SATC, while EuroSAT, CIFAR-I00 and GT- SRB also exhibit substantial improvements acToss all three metrics. These results indicate that the integration of selective textual cues with spatial and visual representations allows SATC to better capture underlying class distributions, thereby improving clustering quality in challenging scenarios where purely visual features may fall short.
Although a few datasets, such as Flowers(Test) and Flowers, show marginally higher ACC or NMI for Clip, SATC still demonstrates superior overall performance rics. The aggregated average values reported in the last row as AVG further confirm the effectiveness and generalizability of SATC. Taken together, these results validate that SATC not only enhances clustering accuracy but also produces clusters that are more consistent with semantic labels, high- lighting its potential robust and versatile approach for large-scale image clustering tasks.
nearly all datasets and met-
Table 5: Clustering performance (ACC% NM1% ARI%) on benchmark datasets.
ACC%
NMI%
ARI %
Dataset
Clip
TAC
SATC
Clip
TAC
SATC
Clip
TAC
SATC
MNIST
56.5
39.8
89.8
54.9
31.5
87.0
35.9
22.3
83.6
USPS
62.3
55.2
85.8
61.8
51.0
80.6
51.2
44.0
73.4
ImageNet-10
98.6
99.5
99.8
96.8
98.5
99.2
97.0
98.8
99.4
GTSRB
31.6
27.7
49.8
43.7
58.3
18.9
Fashion
64.2
70.0
76.0
62.2
66.2
71.2
49.6
56.7
62.8
FER2013
27.0
25.4
31.5
8.5
6.0
9.5
5.7
4.4
7.3
EuroSAT
64.3
64.5
86.3
15.4
53.3
78.9
45.5
50.0
75.1
ImageNett-Dogs
38.9
84.4
91.4
34.4
77.4
89.7
22.6
72.0
86.7
Resise45
67.4
63.2
73.0
70.9
82.6
53.9
50.0
71.1
OxfordPets
52.4
72.7
89.4
66.0
79.1
90.9
40.2
61.2
82.6
DTD
46.5
47.4
57.4
56.2
$7.3
63.4
28.6
32.1
40.4
CIFAR-10
75.8
92.2
94.5
71.8
83.7
88.9
63.4
83.6
88.3
Flowers(Test)
72.3
67.6
67.1
86.7
84.3
85.9
67.0
63.4
63.2
STL-10
94.4
98.3
99.0
92.0
95.7
97.3
89.4
96.3
97.9
Food101
57.2
62.9
64.4
69.0
71.1
41.1
50.7
CIFAR-100
43.0
59.0
63.4
57.1
68.6
70,5
28.1
42.7
48.1
Flowers
75.5
58.6
60.5
87.5
83.2
83.0
66.4
54.1
53.8
TinylmageNet
41.1
46.6
52.0
57.2
61.4
65.1
25.7
31.6
36.8
AVG.
59.4
63.1
73.7
60.9
65.6
76.3
46.3
51.3
63.8
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Published as a conference paper at ICLR 2026
C TIME ANALYSIS
In this section, we present the running time and clustering accuracy comparisons between SATC and the TAC baseline across 18 benchmark datasets. Figure illustrates the results.
It can be observed that SATC generally achieves higher ACC while maintaining competitive or even lower running times compared TAC, demonstrating both efficiency and effectiveness. In partic- ular, SATC shows clear advantages on several challenging datasets such as CIFAR-100, Food101, and TinylmageNet, where the improvements in clustering accuracy are substantial. Moreover, on grayscale datasets such as USPS and MNIST, SATC also achieves remarkably high ACC while of- fering shorter running times compared to TAC. These observations suggest that SATC not only effective in boosting clustering performance but also scalable to datasets of different sizes and com- plexity levels.
Overall, these results demonstrate that SATC consistently provides higher clustering performance while remaining time-efficient across diverse datasets, highlighting its practical applicability for lcage-scale image clustering tasks.
100
TAC
45
SATC
40
80
35
ACC (%)
60
30
@
25
40
20
15
20
10
DTD
USPS
Flowers
Euro SAT
Flowers (Test)
Pets ImageNet
STL -10
ImageNet -10
-Dogs
77%
TAC
400
SATC
80
350
300
ACC (%)
60
40
200
150
20
100
50
0
FER GTSRB MNIST 2013
Tiny CIFAR ImageNet -100
Food 101
Resisc
Fashion
CIFAR
Figure 6: Clustering accuracy (ACC%) and running time comparison across all 18 datasets. For each dataset, the bars represent clustering accuracy (ACC%) for SATC and TAC, while the color intensity corresponds to the running time in seconds.
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Published as a conference paper at ICLR 2026
D VISUALIZATION ANALYSIS
To better understand the impact of each modality on clustering performance, we conducted t-SNE visualizations on OxfordPets and CIFAR-I0 datasets. First, we define five different modality con- figurations for analysis:
ClipKmeans: This method applies K-Means clustering on the raw visual features extracted by CLIP.
W/O Visual: This variant omits the visual modality.
W/O Spatial: This variant omits the spatial modality.
W/O Textual: This variant omits the textual modality.
Tri-Modal: This variant includes all three modalities.
The t-SNE plots in Figure provide an intuitive illustration of clustering behavior under different configurations.
Visual features are essential for clustering performance. As shown in the W/O Visual configuration, without visual features, relying solely on spatial and textual information fails produce meaningful cluster assignments, and the data becomes completely inseparable. This clearly demonstrates that visual features are indispensable, while spatial and textual features serve only as auxiliary cues to enhance clustering performance. Spatial structure significantly enhances discriminability: compar- ing W/O Spatial with Tri-Modal shows that including spatial feature helps form more compact and well-separated clusters, especially for datasets like OxfordPets, where fine- fine-grained object details and relative positions are important. Textual information plays complementary role in refining clustering results. By comparing W/O Textual with Tri-Modal on the CIFAR-10 dataset, observe that beneficial textual information can further enhance image clustering. However, treating all tex- tual information equally useful without selective adaptation may harm clustering performance, as shown on the OxfordPets dataset.
SATC model (highlighted with red bounding boxes) achieving the highest ACC values on both datasets. SATC first demonstrates the ability to accurately discard textual information that may harm clustering performance. Furthermore, by combining visual, spatial, and beneficial textual modalities, the model can achieve better clustering performance. These visualizations qualitatively support our ablation study: each modality contributes uniquely to clustering performance.
OxfordPets
(a) ACC=52.4%
ACC=13.7%
(c) ACC=72.7%
(d) ACC-89.499 (e) ACC=82.6%
CIFAR-10
(f) ACC=75.8%
(g) ACC=19.7%
(h) ACC-92.2%
(i) ACC=93.6%
(i) ACC=94.5%
A. ClipKmeans
B. W/O visual
C. W/O spatial
D. W/O textual
E. Tri-Modal
Figure 7: t-SNE visualizations of clustering results on the OxfordPets (top) and CIFAR-10 (bottom)
under different modality configurations. Red bounding boxes indicate the performance of our pro- posed method SATC.
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Published as a conference paper at ICLR 2026
E Loss FUNCTION ANALYSIS
We investigate how individual loss functions (Cdistil. Coonsist and Leatropy) and their combinations affect clustering performance across six benchmark datasets. These losses designed align cluster assignment distributions across modalities, enforce consistency, and encourage confident assignments, as described in Section 3.3. Their respective contributions and interactions are sum- marized in Figure 8.
When applied individually, the three losses in- fluence the clustering in complementary ways: Cdistill. which aligns the cluster distributions be- tween modalities and plays dominant role in transferring spatial structure knowledge and textual information. Lconsist acts regular- izer, enforcing local consistency across modal- ities and stabilizing training; while it achieves lower standalone ACC, it improves normal- ized mutual information on datasets with high intra-class variability. Centroyy prevents de- generate solutions by promoting confident and well- cluster assignments, but is insuf- ficient alone to capture cross-modal informa- tion. Combining pairs of loss functions im- proves ACC compared to using each loss in- dividually. For example, the combination of Caistill and Lenrroy increases ACC on MNIST to 80.0%, indicating that multi-modal knowledge alignment and entropy maximization work syn-
USPS
ImageNet
Fashion
ImageNet -Dogs
CIFAR
F5
96.30
Eaistil
Loonsist
Centropy
Figure 8: ACC with different loss combinations ergistically. Similarly, the pairs of Lconsist with on 6 benchmark datasets.
Lentopy outperform their respective individual losses, demonstrating that entropy maximization ef- fectively complements consistency. Integrating all three loss functions (Cuisill + Leensist + Lentropy) achieves the best ACC across all evaluated datasets.
From Figure 9, the NMI and ARI metrics exhibit trends consistent with ACC, showing sub- stantial improvements in clustering quality. Taken together, these results indicate that the three losses distillation, consistency, and entropy- interacttte synergistically, indicating that these three losses interact positively and complement each other.
MNIST
77.11
74.76
1351
76.37
37.40
32:18
$7.30
MNIST
$1.29
1460
7.33
435
68
60.15
$3.00
USPS
78,00
12.05
74.6
60.20
79.17
70.10
30.00
USPS
15.m
14300
66.55
70.11
08.78
71.10
ImageNet -10
ImageNet -10
$4.00
37.50
36322
$2.84
50.70
19.20
$0.20
6674
00.00
25.77
00.73
56.32
$8.16
$9.10
Fashion
70.07
10.00
1532
01.00
01.15
71.13
35.70
Fashion
54.90
20.39
8.22
20.00
47.03
03.03
26.00
ImageNet -Dogs
ImageNet -Dogs
74244
$4:11
6.43
81.27
16.30
$1.78
18.30
43.33
11.22
2.74
58.09
83.05
72.27
28.00
CIFAR -10
CIFAR -10
20.00
44.74
10.03
70.20
25.40
$2.17
27.30
7250
2014
3.25
44.04
85.4 04
70.27
27.20
Eaiaal
Eaiail
Emesia
Econsiat
Centropy
Centropy
(a) NMI
(b) ART
Figure 9: NMI and ARI with different loss combinations 6 benchmark datasets.
19

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Published as a conference paper at ICLR 2026
F DISTILLATION DIRECTION ANALYSIS
Table reports the clustering performance of SATC under different distillation directions across 18 benchmark datasets. S-V denotes distillation from the spatial modality the visual modality, V-S represents distillation from the visual modality to the spatial modality, and S++V corresponds mutual distillation between the two.
Overall, mutual distillation consistently achieves the best results, yielding the highest average scores of 73.7% ACC, 76.3% NMI, and 63.8% ARI, and outperforming either single-directin alternative on most datasets such as MNIST, USPS, Fashion, and CIFAR-100, This superiority can be attributed the fact that spatial and visual representations encode complementary forms of structural and semantic information. The bidirectional exchange of supervisory signals enables both modalities to refine their feature spaces coordinated manner, leading to clusters that are not only more compact but also better aligned with semantic categories.
When restricted to single-direction distillation, the performance becomes more dataset-dependent. S-V often provides advantages datasets such CIFAR-10, Flowers, and Tiny Mnageee,e,t, where the spatial clustering head captures low-level structural cues that help the visual modality avoid col- lapsing into overly
groupings. In contrast, V-S achieves stronger results and visually diverse datasets such as ImageNet-Dogs, EuroSAT, and OxfordPets, where the visual modality offers richer semantic guidance that stabilizes the spatial clustering head. Despite these complementary strengths, both single-direction strategies lack the reciprocal feedback that allows each modality iteratively correct the other's biases.
complex
The general trend across ACC, NMI, and ART therefore consistent: leveraging bidirectional dis- tillation between spatial and visual modalities provides the most robust clustering performance. By jointly exploiting the semantic richness of visual features and the structural discrimination of spatial features, mutual distillation enables SATC to learn more discriminative and semantically consistent clusters, especially in challenging datasets where single-direction supervision proves insufficient.
Table 6: The clustering performance (ACC% NMI% / ARI%) ) of SATC with different distillation directions on 18 benchmark datasets. S:Use the spatial clustering head to generate the final cluster assignments.
ACC%
NMI%
ARI %
Datasct
S-V
V-S
S++V
S-V
V-S
S++
S-V
V-S
S++ V
MNIST
78.8
88.4
89.8
77.9
85.2
87.0
68.3
81.7
83.6
USPS
78.4
79.7
85.8
80.9
77.8
80.6
73.6
68.2
73.4
ImageNet-10
99.8
99.7
99.8
99.3
99.2
99.2
99.5
99.4
99.4
GTSRB
37.0
30.6
36.2
58.6
53.4
58.3
29.3
24.5
28.1
Fashion
72.0
68.8
76.0
70.9
66.5
71.2
61.5
56.4
62.8
Fre2013
29.2
28.7
31.5
8.6
7.5
9.5
EuroSAT
80,8
88.7
86.3
76.2
81.0
78.9
71.1
78.4
75.1
ImageNet-Dogg
83.3
91.8
91.4
82.4
90.4
89.7
74.7
87.5
86.7
Resisc45
79.7
71.6
81.4
81.7
77.0
82.6
73.6
60.3
71.1
OxfordPets
80.9
87.7
89.4
86.2
90.7
90.9
72.3
82.1
82.6
DTD
52,5
52.1
57.4
62.0
61.5
63.4
37.2
37.0
40.4
CIFAR-10
95.0
93.8
94.5
88.7
86.6
88.9
89.3
87.0
88.3
Flowers(test)
68.3
66.6
67.1
85.9
85.4
85.9
63.4
61.7
63.2
STL-10
98.9
98.8
99.0
97.1
96.9
97.3
97.5
97.3
97.9
food101
65.2
63.6
69.7
70.4
71.1
49.8
49.1
50.7
CIFAR-100
59.0
56.4
63.4
69.0
67.3
70.5
45.0
42.5
48.1
Flowers
73.2
72.3
60.5
87.2
87.0
83.0
64.7
64.4
53.8
TinylmageNet
53.5
51.8
52.0
65.9
64.9
65.1
38.4
36.9
36.8
AVG.
71.4
71.7
73.7
74.9
74.9
76.3
62.0
62.2
63.8
20

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Published as a conference paper at ICLR 2026
G SPATIAL FEATURE ANALYSIS
To better understand the role of spatial feature in image clustering, we present the detailed cluster- ing performance each dataset. From Figure 10, it can be observed that clustering using visual features (+V) and spatial features alone (+S) achieves comparable overall performance across the 18 datasets, indicating that both modalities capture valuable but distinct information. Considering the seven representative datasets, they can be roughly grouped into three categories: handwritten digits (MNIST, USPS), natural object images (OxfordPets, CIFAR-10. 10, TinylmageNet), and uerial/remote- sensing images (FuroSAT, Resisc45). For the handwritten digits, ACC is 56.5% (+V) 41.3% (+S) on MNIST and 62.3% vs. 67. 67.0% on USPS, showing that spatial features already capture much of the structural information. For natural object images, ACC is 52.4% vs. 82.0% OxfordPets, 75.8% vs. 77.1% on CIFAR-1O, and 41.1% % vs. 40.5% on TinylmageNet, indicating that visual fea- tures generally stronger but spatial features remain complementary. For aerial/remote-sensie images, ACC is 64.3% vs. 66.1% on EuroSAT and 67.4% vs. 47.2% on Resisc45, suggesting that both modalities contribute meaningful structural and semantic cues.
When the two modalities are combined through mutual distillation (+V+S), the clustering perfor- mance consistently surpasses that of using single modality. For instance, ACC increases to 89.8% (+V+S) on MNIST, 85.8% on USPS, 89.4% on OxfordPets, 93.6% on CIFAR-IO, 47,4% on Tiny- ImageNet, 86.3% on EuroSAT, and 81.4% Resisc45. This demonstrates that visual and spatial features provide complementary signals: the spatial modality enhances structural discrimination, while the visual modality enriches semantic understanding of spatial patterns.
Overall, these results empirically demonstrate that integrating visual and spatial modalities through mutual distillation significantly improves clustering quality across diverse datasets. The combined representation not only achieves higher accuracy but also enhances the robustness of the image modality in downstream clustering tasks, highlighting the benefits of leveraging both visual and spatial information.
100
+V+S
60
20-
80-
40
20
80
40-
35
Figure 10: Comparison of clustering performance using three settings: visual features with K- Means (+V), spatial features with K-Means(+S), and combination of visual and spatial features with mutual distillation (+V+S).
21

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Published as a conference paper at ICLR 2026
SATC ALGORITHM FRAMEWORK H
Algorithm SATC for Image Clustering
Notations:
N: number of samples
d: feature dimension predefined by CLIP
K: number of class in the object dataset
Textual compactness
ACC: clustering accuracy
p: patience counter for early stopping
Input: Target raw image dataset D {In \N
Process:
1: Extract visual features visual from D using CLIP.
2: Extract high-level features from D using pretrained ResNet-50.
3: Flatten high-level features to form node features X E R
4: Feed X and edge set E into GAT to obtain updated node features X',
5: Apply global average pooling over X' to get spatial embedding "patial E Rd
6: Extract textual features textual from using Eq. 3.
7: Compute textual compactness from 2eextual using Eq.
8: Freeze the pre-trained (eatures(=>isul)) >-pparial, /textual).
9: if O.33(Computed in Table 2) then
Enable the textual feature ; textual
11: else
12: Do not use textual
13: end if
14: for
epoch = to 200 do
Project visuul >spatial
(and textual enabled) into cluster assignment distributions
visuull eppptiill textual via MLP cluster heads.
Compute total loss C using Eq. 6. Update cluster heads via backpropagation.
17:
Evaluate ACC.
19:
if ACC improves then
Update the best ACC and reset p 0.
21: else
22:
Increment p + +-p+t + end if
23:
24: p 2 10 then
25:
Break
26:
end if
27:
end for
28: Output: Final visual cluster head cluster assignments C'
22

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Published as a conference paper at ICLR 2026
1 TOP-30 DISCRIMINATIVE NOUNS FOR FOUR REPRESENTATIVE REPRESENTATIVE DATASETS
To better understand why textual feature help or hurt clustering performance across datasets, we analyze the discriminative nouns extracted from the textual feature. Specifically, for two datasets where textual features are utilized (CIFAR-IO, STL-10) and two where they excluded (MNIST, FER2013), we compute image-noun softmax retrieval weights, rank nouns by their aggregated weights after deduplication, and take the top-30 nouns to form the discriminative noun set and corresponding word cloud.
Table 7 presents the top-30 discriminative nouns selected for different datasets. For datasets where text was beneficial, such as CIFAR-10 (a dataset of natural object images) and STL-10 (a dataset of natural object images designed for unsupervised learning), the selected nouns correspond closely to actual object categories, indicating high textual discriminability. The word cloud visualizations in Figure 11 further illustrate these patterns. In CIFAR-I0 and STL-10, the nouns align well with object categories, and the word clouds are dense, reflecting high semantic diversity and strong textual discriminability. In contrast, for datasets where text was harmful, such as MNIST (handwritten digit images) and FER2013 (facial expression images), the nouns are mostly unrelated to the visual content, and the corresponding word clouds are sparse, indicating poor textual diversity and weak textual discriminability.
Notably, the four word clouds are arranged order of increasing T value; higher corresponds to more diverse and complementary textual clusters, which is visually reflected in the richer, denser word clouds of CIFAR-I0 and STL-10. This supports our motivation: datasets with higher 7 tend to have textual feature that contribute more meaningfully clustering performance.
Natalie Wood
Andrew ew Fi Fielding Huxles ey Cn
Definite_integgee
Alexx Muwler erynnnnngg
double_dig _igit
Cort_cloud
Frik_Weisz frikkWeeiszLnus Linus_ Carl Paallallngg
Maria
Callas
soanding rodess
triskaidekaphobia
Herbert Ceebbert_aarrue Marcuse
Commissaire Maigret
ret
Devanagari_ Secgari_scriptt
aephos
commi t teewoman
Yevgeni
Alekanndroo oovich Yevtushenko
Ar Aramaic_scrips
Lilliam an
Ballian_Giss
Hebr eww script
Willie
Aleksandr
Prokhorov
Howard d __d___Mas___
sneeze
sneezing
roseola infenturr =
three
ee
year _old two _ear
old assemblywoman
letter
Alicia
J0LL wwn.eraattvii
(a) MNIST
(b) FER2013
minicar
P Pembroke Welsh corgi
dnuhte-propel
black-tailed deer
whip
acarpann
hearing
dog d horse
whitetail deer Tennessee walker trucking
Tennessea see Wa walking
hor
se
rig
single-propeller
merchane
plane electric vessels ss
aut autorobile
sincle-propelles plane
Cypr pporipediumg
um a aluu
black taice-tailddddee
boa_aa
salling war ship
nándeae
dipper struch
Freehe
harness
horse
cat techopriphl
sand
waterbuck pil lot boat
trailer
automobile
electris autonebale
tipper lorry
andke---
proplllle. palane
fizgig
jaguarundi
maberizaa
lat cat
Schoeniclus
cudio
garbage truck
tng attlerwituee
Emberi
Herdiiin Tipper_truct truck
S
Sparron
roe
deer trucking company
Averican_saadoee
car car_loan_ owroowyyyy.oooo
swamp
aircraft
spar row
Havollia
subcompact car
ng car
leopar
Warkrati
cat
door
sesplane
Cardigan Welsh corgi gi
Lipizzan
terrter
guenon monkey
Felis
serval
subcompact car sable
antelope
floatplan
black-and tan coonhound
slectric
ferrybost puppy_ fat Jerbo
articulated bu bu wptpppppeeerrreeeeeeeeeeeeeeeeee
centaiieera..
colobus bacbbuu_oooeeyaaaeee
tit monkey
monkey black--acckk-daaaa waaek--and-aaattrrrrree tarriar consstieerrt
whitetail deer
(c) CI
(d) STL-10
Figure 11: Word cloud visualizations of discriminative nouns for four datasets. The size of each word corresponds to its occurrence frequency in the textual features.
23

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Published as a conference paper at ICLR 2026
Table 7: Top-30 selected discriminative nouns for different datasets, categorized by the effect of text information.
Text Effect
Dataset
Selected Discriminative Nouns
Beneficial
CIFAR-10
Emberiza schoeniclus, Cypripedium album, Tennessee walking horse, narrowbody aircraft, automobile loan, Pembroke Welsh corgi, container ship, sable antelope, pilot boat, Cardigan Welsh corgi, tipper lorry, whitetail deer, puppy fat, Lipizzan, articulated lorry, Tennessee walker, fizgig, subcompact car, swamp sparrow, car loan, single-propeller plane, jerboa rat, American saddle horse, tipper truck, black-tailed deer, waterbuck, sailing warship, trailer truck, ferryboat, field sparrow
Beneficial
STL-10
merchant vessels, Emberiza schoeniclus, trucking company, guenon monkey, whitetail deer, harness horse, hearing dog, draft horse, subcompact car, garbage truck, leopard cat, floatplane, nonsmoking car, sand cat, roe deer, jaguarundi cat, colobus monkey, Black-add-tt tan coonhound, black-tailed deer, single propeller plane, electric automobile, double proolele plane, black- and--ndd--a tan terrier, seaplane, Felis serval, Lipizzan, monkey, Hawaiian honeycreeper, waterbuck, trucking rig
Harmful
MNIST
Devanagari script, Hebrew script, Aramaic script, definite integral, double digit, triskaidekaphobia, Oort cloud, runic letter, back-number, sounding rocket, box number, numbering, Lightnnng Hurler, optic disk, fertilized ovum, lyre snake, Tetragrammaton, twenty-seven, twenty-three, cometfish, equation, twenty-two, operculum, Edward Lear, absorption spectrum, endodontia. needlefish, Hawaiian honeycreeper, retinoblastoma, pied-billed grebe, diamine
Harmful
FER2013
Lillian Gish, Alicia Alonso, Willie Howard Mays Jr., Andrew Fielding Huxley, Linus Carl Pauling, committeewoman, three-year-old, Karl Alex Muller, two-year-old, Commissaire Maigret, Natalie Wood, Erik Weisz, ussemblywoman, Yevgeni Aleksandrovich Yevtushenko, Aleksandr Prokhorov, yawning. roseola infantum, sneezing, Herbert Marcuse, Julius Winfield Erving, sneeze, Caitra, newswoman, Maria Callas, nephew, toddler, Sir Clive Marles Sinclair, Malcolm X, newspaperwoman, turps, Tupac Amaru Revolutionary Movement
24

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Published as a conference paper at ICLR 2026
J COMPARISON OF VISUAL AND VISUAL+TEXTUAL CLUSTERING PERFORMANCE
To investigate how textual features affect clustering, we compare K-Means performance on visual features alone (+V) versus visual features distilled with textual features (+V+T). Visual features are extracted using CLIP ViT-B/32, and textual features obtained following TAC (Li et al., 2023). This setup enables us to evaluate when textual information enhances or hinders clustering.
Table 8 provides detailed comparison of clus- tering performance using only visual features (+V) versus combining visual and textual fea- tures (+V+T)) The results are divided into two groups: datasets where textual features degrade performance (top section) and datasets where they provide significant improvements (bottom section).
Table Clustering aceuracy (ACC %) with visual (+V) and vistal+textual (+ (+V+T) features.
Dataset
+V
+V+T
MNIST
56.5
39.8
USPS
62.3
55.2
GTSRB
31.6
27.7
For datasets like MNIST, USPS. GTSRB, FER2013, and Flowers, incorporating textual features actually reduces clustering accuracy, indicating that the textual descriptions either lack discriminative power or introduce seman- tic noise that misguides the clustering process. Conversely, for datasets such as ImageNet- Dogs, CIFAR-10, STL-10, and OxfordPets, the addition textual feature leads to substantial performance gains, demonstrating the value of multimodal integration when textual features semantically meaningful and well-aligned with visual content.
FER2013
27.0
25.4
Resisc45
67.4
63.2
Flowers(Test)
72.3
67.6
Flowers
75.5
58.6
ImageNet-10
98.6
99.5
Fashion
64.2
70.0
EuroSAT
64.3
64.5
ImageNet-Dogs
38.9
84.4
CIFAR-10
75.8
OxfordPets
52.4
72.7
DTD
46.5
47.4
STL-10
94.4
98.3
Food101
57.2
62.9
CIFAR-100
43.0
59.0
TinyImageNe
41.1
46.6
Overall, while the average performance across all 18 datasets shows a modest improvement with the textual feature (59.4% 63.1%), the spteset-specific variations highlight the impor- tance of selectively incorporating textual guid- ance based on dataset characteristics.
AVG.
59.4
63.1
K COMPARISON OF DIFFERENT MODALITY CLUSTER HEADS
To evaluate the impact of different modality-specific cluster heads on the final clustering assign- ment. We compare three cluster heads: Visual Head (V-Head), Spatial Head (S-Head), and Textual Head (T-Head). For datasets where the textual feature was not selected (r S 0.33), the T-Head is unavailable (N/A). All cluster heads are trained via our mutual distillation framework.
Table 9 compares the clustering performance when using different modality cluster heads for final assignment: Visual Head (V-Head). Spatial Head (S-Head), and Textual Head (T-Head). The results robustly validate our design decision to use the distilled visual cluster head as the primary output. While the spatial and textual cluster heads can occasionally achieve comparable even marginally better results on certain individual datasets (c.g., S-Head on GTSRB and Tiny Imageet, T-Head on Flowers), the distilled visual cluster head demonstrates superior and more consistent performance on average across all benchmark datasets. This outcome confirms that the rich semantic representations from the CLIP visual encoder provide the most reliable foundation for final clustering. The spatial features serve valuable complementary source of structural information during the mutual dis- tillation process, and the textual features. when beneficial (T > 0.33). act effectively as a "semantic teacher." However, the visual modality ultimately proves to be the most robust anchor for producing the final cluster assignments, achieving the highest average performance across all three evaluation metrics.
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Published as a conference paper at ICLR 2026
Table 9: Clustering performance using different modality cluster heads (ACC% NMI% ARI%).
For datasets where textual feature were not selected (r S 0.33), the T-Head was not available (N/A).
The best results for each dataset are in Bold.
V-Head
S-Head
T-Head
Dataset
ACC
NMI
ARI
ACC
NMI
ARI
ACC
NMI
ARI
MNIST
89.8
87.0
83.6
89.5
86.7
83.3
N/A
N/A
N/A
USPS
85.8
80.6
73.4
77.9
81.3
72.0
N/A
N/A
N/A
ImageNet-10
99.8
99.2
99.4
99.8
99.2
99.4
N/A
N/A
N/A
GTSRB
36.2
58.3
28.1
60.4
31.5
N/A
N/A
Fashion
76.0
71.2
62.8
72.4
70.4
61.3
N/A
N/A
N/A
FER2013
31.5
9.5
7.3
27.7
7.6
5,3
N/A
N/A
N/A
EuroSAT
86.3
78.9
75.1
77.2
73,4
66.9
N/A
N/A
N/A
ImageNet- -Dgg
91.4
89.7
86.7
89.4
87.1
83.0
N/A
N/A
N/A
Resisc45
81.4
82.6
71.1
76.9
80.0
66.4
N/A
OxfordPets
89.4
90.9
82.6
82.9
86.2
73.4
N/A
N/A
N/A
DTD
57.4
63.4
40.4
50.3
61.0
35.9
N/A
N/A
N/A
CIFAR-10)
94.5
88.9
88.3
94.4
87.6
88.1
90.2
83.2
82.9
Flowers(Test)
67.1
85.9
63.2
68.1
86.1
61.4
68.6
85.4
63.0
STL-10
99.0
97.3
97.9
98.9
97.1
97.6
97.6
94.3
94.8
Food101
65.2
71.1
60.9
65.7
45.1
CIFAR-100
63.4
70.5
48.1
56.4
68.8
43.3
54.1
64.9
39.1
Flowers
60.5
83.0
53.8
66.0
85.8
59.1
66.4
85,4
59.2
TinylmageNet
52.0
65.1
36.8
53.2
65.8
37.8
50.3
61.8
33.1
AVG.
73.7
76.3
63.8
71.1
75.0
61.7
L THEORETICAL GUIDANCE FOR UTILIZING TEXTUAL COMPACTNESS
Theorem Let V (visual feature), T (textual (eextual and Y (1,2,.... K) (classification label, K 2 2) be random variables. Denote the Bayes classification error rate based on (V,T) as Riv ming PV)(V,T) Y), where the minimum is taken over all measurable functions g. Define the function
f(p) Hbin(p) + plog(K -), D [0, 1/K],
where Hbin -plogg (1 p) log(1 p) is the binary entropy (logarithms in base 2). Then, Ryy f- ( H(TVV) H(TVVY)),
where f-1 is the inverse of f on [0, 1/K], which is strictly increasing.
Proof The K K-class Fano inequality states that H(YIV, S Hbin (Bb,)))) + Ryy log(K - f(HV:r) From the definition of conditional mutual information,
I(T:YIV) =
and also
I(T;YIV) = H(TV)) -((TV,
Equating the two expressions yields
H(Y H(IVV. = H(TVV) H(TIV,))
which rearranges to
=
Substituting into the Fano inequality gives
H(YIV) H(TIV) H(TIV,Y) & S(
26

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Published as a conference paper at ICLR 2026
The derivative of f
(p) = log 1-p
1-p log(K - -log log (K (kK-1) -)) (K-1),
p
p
For p E 10,1-1/K3 we have (1 - p\/p /p > 1/(K 1), so (p) > 0. At the endpoints, f is
continuous and strictly increasing on [0, 10,1-12 1/K]. Since RiTT E [0,1- 1/K), the inverse f-1 exists and is strictly increasing on the range of f. Applying f-1 to both sides of the inequality completes the proof.
This theorem provides an information-theorrtic lower bound on the Bayes error rate multimodal classification tasks. In particular, when the textual feature I exhibits higher uncertainty (high value) given the visual feature V (i.e., larger / ()),,, the theoretical lower bound the Bayes error rate Rे becomes smaller, indicating a higher potential upper bound for classification perfor- mance.
M EVALUATING THE CONTRIBUTION OF RESNET-50 TO MODEL PERFORMANCE
CLIP could extract both image-level and patch-level features. However, since it is primarily opti- mized for image-text aligument in a shared semantic space, its representations tend to emphasize global semantics at the potential cost of localized spatial details. To address this, introduce spa- tial features in which ResNet-50 extracts local spatial information from image patches, and GAT further captures relational dependencies among these patches.
In order to further analyze whether the performance improvement stems from the method design or from leveraging more powerful pre-trained features, we conducted comprehensive ablation study comparing the following configurations: (1) ResNet-50 only: K-Means on features from the ImageNet--retraine ResNet-50. (2) CLIP only: K-Means on CLIP visual features (our baseline). (3) Simple Fusion (CLIP + ResNet-50): K-Means on concatenated features from CLIP and the Imageeett-retrrined ResNet-50. (4) CLIP Spatial Features: K-Means on concatenated CLIP vi- sual features and our proposed spatial features. (5) CLIP + ResNet-50 + Distillation: Our framework uses CLIP visual features and ResNet-50 features with mutual distillation. (6) Full SATC (Ours):
The complete pipeline.
Table 10 reports the results of this ablation study. The results presented in Table 10 clearly demon- strate that: (1) Using ResNet-50 features alone (Column 1) performs significantly worse than using CLIP features alone (Column 2). The average accuracy drops from 60.8% to 51.3%. (2) Simply con- catenating CLIP and ResNet-50 features (Column 3) provides negligible improvement over CLIP alone (59.4% vs. 60.8%), and even degrades performance on several datasets. This indicates that naively adding more pre-trained features is not beneficial. (3) In contrast, using our spatial features with CLIP (Column 4) already yields a substantial gain (64.0%), confirming the value of the struc- tural information captured by our encoder. (4) The full SATC pipeline (Column 6) achieves the best performance (73.7%). The step-by-step improvement from Column 2 to Column 4 to Column 6 demonstrates that the performance gain is primarily attributable to our novel integration of spatial structure and selective textual guidance, rather than the mere use of additional pre-trained features. These results provide evidence that the improvement indeed consequence of our method's de- sign.
27

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Published as a conference paper at ICLR 2026
Table 10: ACC Results of Ablation study on the Effects of integrating ResNet-50
2
3
4
5
6
CLIP ResNet-50 Distillation
V
V
V
V
V
V
V
GAT
V
I
50.1
56.5
56.8
57.5
75.2
89.8
2
61.4
62.3
61.5
62.7
78.2
85.8
3
97.0
98.6
98.8
99.4
99.6
99.8
4
26.7
31.6
32.2
33.6
34.7
36.2
5
58.8
64.2
64.3
64.4
72.5
76.0
6
26.4
27.0
27.2
27.8
30.7
31.5
7
56.8
64.3
64.6
64.7
81.6
86.3
8
33.3
38.9
40.0
74.5
84.1
91.4
9
54.3
67.4
67.7
64.3
78.3
81.4
10
47.8
52.4
55.0
72.7
75.1
89.4
11
39.7
46.5
44.5
48.3
48.5
57.4
12
55.3
92.2
76.2
86.3
94.0
94.5
13
66.9
67.6
70.9
72.6
67.2
69.7
14
88.9
98.3
94.5
98.1
98.7
99.0
15
42.8
62.9
56.5
56.8
61.4
65.2
16
25.0
59.0
42.2
47.0
59.3
63.4
17
67.5
58.6
74.7
74.9
55.3
60.5
18
25.2
46.6
41.1
45.5
49.3
52.0
Avg.
51.3
60.8
59.4
64.0
69.1
73.7
28


Reviews:
[{"review_id": "q9nTA7luAr", "reviewer": "Reviewer_dWnF", "summary": "This paper proposes SATC (Spatial structure and Selective Text for Clustering), a tri-modal image clustering framework integrating visual, spatial, and textual information. It employs a GAT-based spatial encoder to capture relational dependencies among image patches and a compactness-aware textual feature selector to adaptively incorporate useful textual cues. These modalities are fused through tri-modal mutual distillation to improve clustering quality. Experiments on 18 benchmark datasets show that SATC consistently outperformed state-of-the-art methods such as TAC and SPICE in accuracy, robustness, and efficiency.", "questions": "1. Apart from TAC, are there any newer multi-modal clustering methods, such as the multi-modal alignment model for 2024-2025? Why aren't these methods taken into account for comparison?\n2. The paper mentions the use of Graph Attention networks (GAT) to capture the spatial relationships between image patches, but does not elaborate on why GAT was chosen instead of other types of graph neural networks or transformer. What is the basis for choosing GAT?\n3. The paper mentions that text feature selection is based on compactness (τ) and sets a fixed threshold (τ = 0.33). If the threshold is lower than this, the use of text information is abandoned, and only spatial and visual information is used. In this case, the authors believe that the text information may have provided negative benefits. However, the text-guided image clustering methods have been affirmed in some compared papers. How can this be explained?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.Originality:\nThe idea of combining spatial structure modeling with selective textual guidance offers a reasonable and incremental improvement over existing CLIP-based clustering frameworks.\n2.Quality:\nThe methodology is technically sound and well-executed. The design of the spatial encoder, textual selector, and tri-modal distillation is coherent, and the experiments are comprehensive.\n3.Clarity:\nThe paper is generally well-written and logically structured. The framework and algorithms are clearly explained, supported by intuitive figures and detailed appendices.\n4.Significance:\nThe proposed method achieves consistent improvements across 18 datasets, showing robustness and general applicability. The approach offers a practical advancement in multi-modal unsupervised image clustering.", "weaknesses": "1. Limited theoretical grounding for the compactness threshold (τ=0.33) — while empirically validated, a more formal justification or sensitivity analysis would strengthen the argument.\n2. Underdeveloped analysis of failure cases: The paper could better analyze cases where text features hurt performance (e.g., CIFAR-10), which would strengthen the argument for “selectivity.”\n3. Comparative baselines: Recent multi-modal clustering approaches beyond TAC (e.g., self-supervised multi-modal alignment models from 2024–2025) are not included.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "i9jZkaRpvA", "reviewer": "Reviewer_GM72", "summary": "The work combines three different modalities (visual, spatial, textual) to enhance clustering performance on a variety of image datasets. One key contribution is the newly introduced spatial modality that encodes relationships between image patches. To effectively leverage the different modalities a new framework is introduced to enforce cross-modal alignment in image clustering. Additionally, the authors establish an adaptive textual feature selector that estimates the benefits of using textual features during clustering. This prevents performance degradation on datasets where textual descriptions are uninformative or misleading. The experiments report SOTA performance across the vast majority of the 18 datasets.", "questions": "1. Please provide the standard deviations for all datasets to estimate the statistical significance of the reported improvements.\n2. What specific relational dependencies do the spatial features capture that CLIP's ViT doesn't already encode?\n3. How was the 0.33 threshold determined? Based on the train/val split or post-hoc on the test data? Why does the usage of textual features need to be a binary decision rather than being modeled by weights determining their impact?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "1. The novel textual feature extractor is well motivated and proves to be of great benefit to the clustering performance\n2. Comprehensive empirical validations were made on various datasets\n3. SOTA results on a vast majority of datasets", "weaknesses": "1. The results are missing standard deviations to estimate the actual statistical significance of the proposed method\n2. Are spatial features really a contribution or could visual-textual be sufficient? An ablation studies on the impact of the addition of spatial features for clustering would be great.\n3. The compactness metric threshold appears to be found through exhaustive search rather than principled derivation\n4. Typo in 324/328.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UOH5ZziBOZ", "reviewer": "Reviewer_eiRh", "summary": "Based on the externally guided clustering paradigm, this paper further leverages spatial information to distinguish visually and textually similar instances. The proposed method is extensively evaluated on 18 image clustering datasets, demonstrating superior performance over previous studies.", "questions": "My major concerns lie in whether the performance gain comes from the proposed method additionally leverages a pre-trained ResNet-50 model. Besides, some details of the proposed method should be explained more clearly. I will raise my score if my concerns are well addressed.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The contribution of this work is clear, i.e., leveraging the spatial feature in addition to visual and textual features to facilitate image clustering. Such a motivation is straightforward.\n2. Extensive experiments across 18 datasets demonstrate the effectiveness of the proposed method.\n3. The ablation study on incorporating textual semantics with the compactness metric is interesting.", "weaknesses": "1. The writing in section 3.1 is confusing. Where exactly is the graph attention applied? On different images, or on patches within a single image?\n2. Besides the pre-trained CLIP model, a pre-trained ResNet-50 model is also utilized in the proposed method. It is questionable why ResNet-50 is needed, since CLIP could already extract both image- and patch-level features. Does the performance improvement of the proposed method come from introducing the ResNet model?\n3. How are the textual compactness metrics in Eq. 5 used? It should be explained more clearly in the subsection.\n4. Since the proposed SATC is more efficient than TAC, experimental results on the full ImageNet-1K are expected.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "m5aj2NwDoU", "reviewer": "Reviewer_nD9y", "summary": "This paper studies the task of image clustering. It introduces a framework named **Spatial Structure and Selective Text Jointly Facilitate Image Clustering, a novel framework desigend to overcome the limitations of existing CLIP-based deep clustering methods, particularly regarding their representation of spatial structures and indiscriminate use of text features.** \n\nThe framework fuses visual, spatial, and selectively chosen text features using a designed Tri-modal mutual distillation strategy. \n\nExtensive experiments across 18 benchmarks show SATC’s good performance and effectiveness.", "questions": "**Q1:** The Tri-modal mutual distillation framework utilizes three loss types: distillation ($L_{distill}$), consistency ($L_{consist}$ with $\\lambda_1=1.0$), and entropy ($L_{entropy}$ with $\\lambda_2=5.0$). What specific theoretical or empirical justification underpins the choice to set the **weight of the entropy loss ($\\lambda_2$) five times higher** than the consistency loss ($\\lambda_1$)?\n\n**Q2:** The final cluster assignments are consistently produced by the **distilled visual cluster head**. How would the clustering performance be affected if the final assignment were instead derived from the distilled spatial cluster head or the distilled textual cluster head, especially given that mutual distillation is shown to be superior overall?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Superior Clustering Performance: The proposed method consistently achieved highest clustering results compared to extensive prior works across 18 benchmarks, showing substantial improvements.\n2. The idea of incorporating spatial structure is interesting and effective.\n3. The idea of textual selection is also insightful\n4. Efficiency and Scalability: SATC not only achieves higher clustering accuracy but also maintains competitive or even lower running times compared to the TAC baseline across most datasets", "weaknesses": "**Potential Flag:** The Use of Large Language Models (LLMs) is **not disclosed** in the current manuscript, which is a violation of the new rule imposed by ICLR this year.\n\n**W1:** Experiments on novel, unseen datasets are needed. All evaluated datasets in the current work might be explicitly leveraged during CLIP training. Therefore, it is hard to confirm the effectiveness of the proposed framework without control experiments on complete unseen, novel images. It is practical and important because clustering methods are often used to explore and understand unseen, novel images without labels.\n\n**W2:** The authors discussed the pros and cons of visual and textual modalities in CLIP, and their effects on image clustering. Why not compare with DINOv2, v3 for image clustering under the same model size? A comparison with DINOv2 and v3 that uses a single modality is necessary. KMeans + DINOv2 / v3 features is a good baseline.\n\n**W3:** The textual feature selector and tri-modal objective function are both depent on empirically set threshold $\\tau$ and $\\alpha$. How do the authors select these hyperparameters? What are the selection criteria or dataset? The authors mentioned it is “based on extensive experiments” at Line#331,  however, If all parameters tuned on the  test set, it is unfair.\n\nIf the above primary concerns could be addressed during the discussion stage, the reviewer is open to rise the rating.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)