MetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning
Authors: Hongjia Liu
Rongzhen Zhao
Haohan Chen
Joni Pajarinen
Venue: NeurIPS 2025 poster ()
Keywords: Object-Centric Learning
Slot Attention
Unsupervised Learning
Vector Quantization
self-supervised learning
Primary area: deep_learning
TL;DR: MetaSlot is a plug-and-play Slot Attention variant that adapts to variable object counts by deduplicating slots using a learned prototype codebook, significantly improving object-centric learning performance and interpretability.

Abstract: Learning object-level, structured representations is widely regarded as a key to better generalization in vision and underpins the design of next-generation Pre-trained Vision Models (PVMs). Mainstream Object-Centric Learning (OCL) methods adopt Slot Attention or its variants to iteratively aggregate objects' super-pixels into a fixed set of query feature vectors, termed slots. However, their reliance on a static slot count leads to an object being represented as multiple parts when the number of objects varies. We introduce MetaSlot, a plug-and-play Slot Attention variant that adapts to variable object counts. MetaSlot (i) maintains a codebook that holds prototypes of objects in a dataset by vector-quantizing the resulting slot representations; (ii) removes duplicate slots from the traditionally aggregated slots by quantizing them with the codebook; and (iii) injects progressively weaker noise into the Slot Attention iterations to accelerate and stabilize the aggregation. MetaSlot is a general Slot Attention variant that can be seamlessly integrated into existing OCL architectures. Across multiple public datasets and tasks--including object discovery and recognition--models equipped with MetaSlot achieve significant performance gains and markedly interpretable slot representations, compared with existing Slot Attention variants.
The code is available at https://github.com/lhj-lhj/MetaSlot.

MetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning
Hongjia Liu' Rongzhen Zhao' Haohan Chen Joni Pajarinen'
'Dpparteent of Electrical Engineering and Automation, Aalto University, Espoo, Finland DDepartment of Computer Science, Sichuan University, Chengdu, China (hongjia.lin rongzhen zhao, joni. wawaaaannnaaaaaaaaaaaaa. sajel@stu. scu. edu.
Abstract
Learning object-level, structured representations is widely regarded key bet- ter generalization in vision and underpins the design of next- gexerraoenttionn Pre-trained Vision Models (PVMs). Mainstream Object- Centric Learning (OCL) methods adopt Slot Attention or its variants to iteratively aggregate objects' super-pixels into fixed set of query feature vectors, termed slots. However, their reliance a static slot count leads to an object being represented as multiple parts when the number of objects varies. We introduce MetaSlot, a plug-and-play Slot Attention variant that adapts to variable object counts. MetaSlot (i) maintains a codebook that holds prototypes of objects in dataset by vector-quantizing the resulting slot rep- resentations; (ii) removes duplicate slots from the traditionally aggregated slots by quantizing them with the codebook; and (iii) injects progressively weaker noise into the Slot Attention iterations to accelerate and stabilize the aggregation. MetaSlot is general Slot Attention variant that can be seamlessly integrated into existing OCL architectures. Across multiple public datasets and tasks-includin object discovery and recognition-mun mooills equipped with MetaSlot achieve significant performance gains and markedly interpretable slot representations, compared with existing Slot Attention variants. The code available at hntps://gihubbcom//hhj///////eea
Introduction
Human intelligence rooted in limited perceptual experience especcilll visual information-whie enables it to demonstrate outstanding transfer and generalization abilities in entirely new task scenarios
12. recent years, major breakthroughs in embodied intelligence have further underscored the inevitable trend of artificial intelligence moving into the physical world (15). However, the key challenge in achieving high-level cognitive reasoning [ and compositional generalization [8] lies in transforming visual inputs into structured, discrete, and independent object-level representations
10-12 thereby granting agents a deep understanding of physical objects and their dynamic relations
13
14
Object-Centric Learning (OCL) has rapidly developed against this backdrop. Its goal is extract object-level structured representations in an unsupervised manner, rather than relying on attribute- level features or global scene features. Among numerous methods 20), Slot Attention (SA) [1] is currently the most influential and widely adopted. Through a competition mechanism among slots, iteratively clusters distributed scene representations into several object-oriented feature vectors, named slots; each slot can then be decoded separately [ (11. all slots can be decoded jointly to produce semantically consistent segmentation masks. This
in an autoregressive fashion 22 23
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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strategy not only efficiently captures object-level information but also lays solid foundation for subsequent physical reasoning and relational modeling 26)
Nevertheless, classic Slot Attention [11] still suffers from two key limitations: (1) the number of slots must be preset fixed hyper-parameter: (2) slot initialization relies on random sampling. The former conflicts with the dynamic variability of object counts in real visual scenes, easily leading to inder-seementation or over-segmentation and harming the identifiability of the representations 127 28;; the latter often results in object-centric representations that lack a clear correspondence with true object concepts [29]. Overall, fixed slot count and random initialization are equivalent to imposing inappropriate prior assumptions on the latent space, making the model more prone to sub-optimal solutions and limiting its generalization capability.
Vector quantization (VQ) [30 offers a viable pathway: It has recently shown great value in generative modeling by enabling models to extract and reuse semantic structural patterns [3] Inspired by this insight, we incorporate a VQ codebook that supplies globally shared object prototypes, guiding slot initialization structurally; meanwhile, we prune duplicate slots to provide explicit semantic cues about "objects" from the very start of the aggregation process. In particular, this idea of "object prototypes" echoes Plato's 's "world of forms" [34]: every concrete object in the perceptual world is a projection of some eternal and perfect ideal form. Analogously, we regard each prototype vector the VQ codebook as an idealized object concept, whereas the input features are concrete mappings of these forms. Based on this intuition, we propose MetaSlot, a novel object-centric learning framework that employs a unified prior of global prototype slots and a dynamically adaptive two-stage aggregation method to flexibly match the slot count to the objects present in a scene. Specifically, our study introduces two important technical innovations:
Dynamic slot allocation. To address the above limitations, we design the MetaSlot framework to adaptively adjust the number of slots through two-stage aggregation. First, to match input features with the prototype codebook, we perform initial aggregation using Slot Attention in first stage. The resulting slot vectors are then matched to the global discrete codebook, producing semantically consistent discrete slot indices. Next, to allow the model to adjust the effective slot count according to scene complexity, we apply a de-duplication operation to slot indices that correspond to the same prototype. retaining only distinct prototype slots as the initialization for second stage. The object-aware initial slots are then fed into a mask slot attention module for a second aggregation stage, enabling fine-grained object-level assignment. Throughout this stage, the aggregator applies an attention mask to redundant slots and shares the same weights as the first-stage Slot Attention.
Consistent prototypes and stable optimization. To ensure that all parts of the same object converge to a consistent prototype. we use the final slot representations obtained from the second stage to update the codebook. Simultaneously, we employ a k-means-based exponential moving average (EMA) strategy to update the codebook stably and suppress high variance in the early training phase. In addition, we introduce a progressive coise-injection mechanism during training as implicit simulated annealing [35] further reinforcing efficient alignment between the prototype prior and the posterior over targets in latent space.
In short, MetaSlot leverages global VQ codebook of object prototypes. Slot Attention clusters features, aligns them with their nearest prototypes, prunes duplicates, and passes the resulting seman- tically rich slots a second aggregation stage. Moreover, injecting progressive noise during this stage helps stabilize convergence, yielding robust and accurate object representations. Our work makes three primary contributions: (i) MetaSlot module: We devise a two-stage aggregation framework that couples a global vector-quantin quantized codebook with a slot-masking mechanism, enabling dynamic slot allocation for arbitrary numbers of objects. (ii) Progressive noise injection: By injecting gradually diminishing Gaussian noise throughout the Slot Attention iterations, MctaSlot both accelerates con- vergence and stabilizes the aggregation. (iii) Large-scale validation: Extensive experiments across diverse vision tasks and datasets show that MetaSlot yields substantial improvements on key metrics and exhibits strong adaptability to a wide range of scenes.
2 Method
In this section, we present MetaSlot with (i) First-stage Aggregation for Prototype-gait -uiiddd Pruning: (ii) Second-stage Aggregation with Mask-guided Refinement; and (iii) Prototype update via mini-

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Protatype
Update
Znotsy
Sample
Attention
Smild
S
Maskodi
Decoder
Smask
fiimo
shared weights
DINO
MetaSlot
Zyoc
Figure I: Overview of the MetaSlot framework (depicted on the DINOSAUR backbone for clarity;
agnostic to the underlying object-centric architecture). (i) We build and continually update a codebook of "prototype slots" by vector- vector-quantizing slots sampled across the dataset. (ii) Input features Z are first aggregated via Slot Attention to produce an intermediate slot set S we then remove duplicate slots in Suid by matching them against the prototype slots, yielding the masked subset S mask- (iii) Finally, masck is passed through Masked Slot Attention with progressively attenuated noise to generate the refined slots 5final which are then decoded to reconstruct the original input.
batch K-means. Notably, the two aggregation modules share weights and are jointly trained. In addition, we include pseudocode in Appendix provide additional implementation details.
2.1 Background
Slot Attention (SA). Slot Attention (SA) [1] transforms a set of input features Z E R Nx into K object-centric representations R xxD x through iterative cross- coos-attention. Each iteration, slots compete by applying softmax over themselves to claim parts of the visual input, and each slot's incremental information Š is computed as the attention attention-weighted sum of visual feature vectors.
Aij ΣN 1==1 Aij
(k(Z) ((Z))((S)) VD
$ foman (S
(((), where A softmax
E NNK
In the slot update stage, the incremental information Š and the previous slot state ((t) fed into Gated Recurrent Unit (GRU) [36]. The GRU output is then refined by small MLP, yielding the updated slot state:
1(++) MLP(GRU(S"), S)).
After 7' iterations, the final slots S are employed as the object-centric representation passed to downstream modules. Crucially, these slots are randomly initialized from learnable Gaussian distribution N(u diag(a)).
2.2 First-Stage Aggregation for Prototype-guides Pruning
In the first-stage aggregation, MetaSlot performs global prototype aligmment, merging all features of an object into one slot, pruning duplicate slots, and yielding a compact, semantically coherent basis for later fine- greeggr aggregation.
To obtain prototype slot representations from the codebook that faithfully capture the input features, we first perform a preliminary aggregation of the feature maps, producing a of softly assigned intermediate slots mmid Each intermediate slot is then matched to the global discrete codebook via nearest-neighhour search, yielding the semantically aligned discrete slots S.

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Given the intermediate slots S produced by the original Slot Attention [ the fixed number of slots can lead to single object's features being scattered across multiple slots. To resolve this, prototype matching maps every slot encoding the same object onto shared prototype, thereby eliminating redundancy. As a result, among slots with identical indices only the unique prototypes Smask are retained, and this compact set is used to initialize the next stage.
As the original formulation, we sample the initial slots S E R Nx from learnable Gaussian N(u, diag(or), )), and then perform T iterative slot updates on the input feature set Z E FxD
Intermediate slots.
Smid SlotAttnnZ,S T).
Nearest-neighbour quantisation. Let 3 = fer E RD] denote a global codebook of K prototypes. For each intermediate slot vector smiid, we find the index of its nearest prototype and replace accordingly:
idz; = arg min ||
min
ex
8, Eidz,>
where & is the quantised slot vector, set to the prototype eids,
Duplicate-remon removal mask. Slots that pick the same prototype are treated as redundant. We mark the first occurrence and mask out the rest:
1, if idæ; is the first hit,
smask;
(5)
0, otherwise.
The surviving slot set is Smask {&;
ssaak: = 13 Rxd with Ñ S N.
2.3 Second-Stage Aggregation with Mask-guided Refinement
In the second-stage aggregation, refine the pruned prototypes via masked attention with annealed noise injection, producing the final semantically coherent slot set 5final
re-initialize the slot states with the pruned prototype set Smask and perform T iterations of attention-based updates. At each step, the raw attention logits are masked by the binary slot mask smask, so that only retained prototypes participate in computing the attention-weighted slot in- crements. To alleviate the "cold-start" misalignment between prior prototypes and current inputs, we also inject progressively isotropic Gaussian noise into the features before each iteration, with variance of linearly annealed from o to zero. This implicit simulated annealing encourages early exploration and late-stage convergence, yielding the final semantically coherent slot set Sfinsl
Implicit Simulated Annealing via Noise Injection In the classic Slot Attention module, slots aggregate visual features through iterative attention and GRU updates. However, when prototype slots are generated by offline vector quantization (VQ), initial misalignment often occurs between the prior slots and posterior slots derived from the current input the latent space. To reduce this "cold-start" distance, explicitly inject decreasing noise into the features before each iteration. This strategy can be interpreted as a form of implicit simulated annealing: injecting large-magnitude noise at early stages relaxes the entropy constraints of soft matching, encouraging exploration among slots; gradually reducing noise at later stages facilitates convergence to precise alignment. At any iteration step t, we add isotropic Gaussian noise to the features Z E NDD;
(t)
(4) +
(1)
~ N(O, afIc), Siso
(
(6)
poise
Siso
where
01 Quoiee
T-1
(7)

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Eq. 7 corresponds to gradual decrease in temperature 7 x a, and moise is tunable hyperpa- rameter that specifies the initial standard deviation of the injected isotropic Gaussian noise (i.e. the noise amplitude at = 0) before annealing.
Masked Slot Attention (MSA). To ensure that only surviving slots steer the refinement, introduce Masked Slot Attention (MSA). At each iteration, a binary mask smask zeros out rows corresponding duplicate slots, so the attention-weighted update computed solely from the retained, semantically meaningful prototype slots. This prevents any duplicate-inuuced induced interference. is worth noting that the MSA shares the same of weights with the SA used in the first-stage aggregation. For each iteration == O...., . we compute the MSA:
((t) Sam
(4)
noise)
A EN 1
Hoisse)
1 smask; (0)
(8)
with
A softmnx
ki k( moiee g (
R-N
(9)
VD
where q, k, v E DR are the linearly projected queries, keys and values.
Finally, the slot states are updated as in Eq. 2
yielding ((++1).
Gradient Truncation and Bi-level Optimization. Because the vector-quantization (VQ) mecha- nism truncates gradients-produs stop the gradient flow at the first Slot-aggregation stage. In addition, inspired by the bi-level opti- mization strategy [291, we further detach gradients during the first T 1 iterations of the second Slot-aggregation stage.
instability between Two-stage Slot Aggregation iterations, we
Let (!T)) be the slots after the f-th refinement step in second-stage aggregation. Thus all paths that reach the encoder features Z through ( ................................ s((T") are detached, and only the T-th (final)
refinement step ( contributes gradients.
2.4 Prototype update via mini-batch K-means.
To encourage the codebook slots to converge toward identifiable slot prototypes, we use the final s(") to update the codebook. To ensure stable updates to the codebook, adopt K-means-based exponential moving average (EMA) update strategy. Specifically, at each training step we shift every
prototype ex toward the mini-batch centroid C₁ computed over S (TT)
ex (1 - n) ex + 1) Ck:
(10)
where 7/ (0, 1] is small learning rate.
Prototypes that remain unselected for a predefined timeout window are marked as dead. For each such code sample a replacement vector from the current mini-bate by choosing the slot that is least similar (cosine distance) to all active prototypes, and reset the dead code via
EK f C.
(11)
Related Work
In recent years, unsupervised representation learning has made significant progress, with Slot Atten- tion (SA) [ playing pivotal role in advancing this field. SA learns distinct latent representations for each object in image through an iterative mechanism, and these latent "slots" can subsequently
be decoded back into pixel space. Early slot-based methods 211221 37 typically employed simple small-scale CNNs (3 pre-trained ResNet models [39] as feature encoders, and used Spatial
Broadcast Decoders 40 or Vision Transformers [1 as decoders, with tests mainly conducted on
synthetic datasets. Recent approaches such SlotDiffusion [42 and LSD 43 integrate SA with diffusion-moder decoders 4
45
DINOSAUR and its variants 46 48
constructs reconstruction
objectives based on DINO
49 50
features enhance object discovery in wol-d data.
To enhance Slot Attention's 's intrinsic object-awareness, BO-QSA (2) introduces bi-level mechanisms and slot-level initialization, whereas ISA [5]] incorporates pose-equivariance within its attention and
5

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VOC
Ruw
Ours
BO-QSA
Figure 2: Qualitative results show that MetaSlot's dynamic slot allocation mitigates BO-QSA's over-segmentation, such as splitting a train into unrelated parts, due to its fixed slot count.
generative modules. However, these methods remain constrained by fixed number of slots, resulting persistent over-segmentation issues. FT-DINOSAUR 47 mitigates redundancy by selecting the top & most probable slots during the decoding phase, and SOLV [22 clusters aggregated slots to improve semantic consistency. Nonetheless, these approaches rely on heuristic, non-learning-aan based strategies with explicit thresholding during decoding. While AdaSlot [53] directly predicts the number slots from feature maps, it does not achieve quantitative improvements over standard Slot Attention on real-worke datasets. Furthermore, none of these existing methods incorporate explicit object priors or semantic cues during initialization.
On the theoretical front, a number of studies have offered formal interpretations of object-centric learning (OCL) [54] 27 58 In terms of evaluation methodologies, recent works have examined the generalization ability of object-centric representations across various downstream tasks, including visual question answering (VQA) [59], world modeling (2 25 6D 61, and video generation (59
Furthermore, several studies have investigated the robustness of object-centric models under out-of- distribution (OOD) conditions [63 64 47). Other works have explored the use of vector quantization mechanisms to improve object disentanglement and interpretability. For instance, methods such as 665 66 learn hierarchical, compositional discrete representations that align with objects and their attributes.
Experiments
Overall, our study targets two goals: first, to show that MetaSlot-when substituted for vanilla slot enhances the performance of object-centric learning (OCL) models; and second, demonstrate that MetaSlot plugs in naturally both Transformer-base and diffusion- bbasdd OCL frameworks, underscoring its broad applicability. We evaluate its impact on two canonical tasks: object discovery, which demands pixel-accurate masks for every object instance, and set prediction, where classification accuracy reveals how much object information the slots capture and thus reflects quality of the learned representations.
Datasets We include both synthetic and real-world datasets. ClevrTex 67 comprises synthetic images, each with about 10 geometric objects scattered in complex backgrounds. MS COCO 2017 68 a recognized real-world image dataset, and we use its challenging panoptic segmentation and instance-level object annotations. PASCAL VOC 2012 (69 is real-world image dataset, and we use its instance segmentation. We also report results on the real-world video dataset HQ-YTVIS (70), which contains large-scale short videos from YouTube.
To eliminate confounding implementation differences, we re-implemented every baseline from scratch rather than reusing published results. All experiments share identical data augmentation pipelines and use the DINOv2 ViT(s/14) [50] as the OCL encoder, with matched
Training Details

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training hyperparameters. Every pondel-including both the baselines and our variants augmented with MetaSlot-was trained for 50 steps with the Adam optimizer [7]] on a single NVIDIA V100 GPU using 16-bit mixed precision and a batch size of 32; the MetaSlot codebook size was fixed to 512 throughout. As the most advanced publicly available aggregator currently surpassing vanilla Slot Attention, BO-QSA [29] is adopted as the default module in all baselines. This uniform setup ensures fair and reproducible comparisons, enabling precise evaluation of MetaSlot's contribution. All reported results are averaged over three random seeds to mitigate stochastic variance.
4.1 Evaluate Object Discovery
Models We integrate MetaSlot into object-centric learning (OCL) frameworks and systematically benchmark it against range of classic models (Table I as well as state-of-the-ar models (Table 2) to highlight its performance gains. Concretely, SLATE [22] employs a Transformer decoder for autoregressive reconstruction. DINOSAUR 46 uses an MLP-based hybrid decoder to reconstruct directly in DINO feature space, while VideoSAUR 722 adapts this design to video. SlotDiffusion [42] performs decoding with a conditional diffusion model, and SPOT [73 combines nine permutation- based Transformer decoders with a trainiga strategy. Evaluating MetaSlot within each of these heterogeneous decoding paradigms enables comprehensive assessment of its versatility. We exclude IODINE [49]. ISA 74), SAVi (21), SAVi++ (37. and Mo'ook 775 due to outdated performance or reliance multi-moda priors that hinder fair comparison under our unified setting. Appendix E presents a comparative evaluation of MetaSlot against AdaSlot [53), SlotContrast (66), SysBinder (65), and 666 across multiple datasets, further demonstrating the effectiveness of MetaSlot.
The object-discovery task provides straightforward view of how effectively individual slots separate distinct objects. Following standard practice in OCL research, we assess representation quality by comparing the mask assigned to each slot with the instance-lever ground-trun masks. Concretely, we report the Adjusted Rand Index (ARI) and Foreground Adjusted Rand Index (FG-ARI) 77 to measure clustering similarity, and evaluate mean Intersection-over -over-unon (mloU) and mean Best Overlap (mBO) to quantify how well the discovered masks align with the real objects.
Metrics
Analysis To comprehensively evaluate the effectiveness of the proposed MetaSlot aggregator, we in- tegrate into several mainstream OCI decoding frameworks (MetaSlotynp- MetaSlotma MetaSlotps:) and directly compare it against their original implementations (DINOSAUR, SLATE, SlotDiffusion).
As shown in Tablc[ MetaSlot consistently outperforms its corresponding baseline across all de- coding frameworks. In the MLP setting, MetaSlotaip yields higher decoding accuracy and better reconstruction quality than DINOSAUR. Under the autoregressive setting, MetaSlotqıa achieves substantial performance gains over the original SLATE. Similarly. in the diffusion -ased framework, MetaSlotprz consistently surpasses SlotDiffusinn. These results demonstrate MetaSlot's 's strong com- patibility and generalization ability across diverse decoder architectures, highlighting its robustness and superiority in complex visual reconstruction tasks. We also visualize the ebjeet-segmennttioon results of MetaSlotMap on the COCO and VOC datasets in 2 The examples show that MetaSlot performs dynamic slot allocation effectively, eliminating the over-segmentation problem that afflicts the DINOSAUR baseline, whose BO-QSA [29] aggregator enforces fixed number of slots.
Furthermore, as shown in Table 2 we compare MetaSlot with recent state-of-the-arr methods. Because our goal is not to challenge the entire model architecture but to isolate the impact of the aggregator module. Therefore, the decoder from SPOT in single training round, without adopting the two-stage self deff-distillation strategy proposed in the original work. Remarkably, even under this simplified training setup, our method achieves comparable or even superior performance across all evaluation metrics. Similarly, when comparing with VideoSAUR on the YTVIS(HQ) dataset, simply replacing its aggregator with MetaSlot yields substantial performance improvements setoovements-pariculaalyy in FG-ARI (+ (+13.3) and mBO (+2.9). These results highlight MetaSlot's adaptability, proving effective in both static and video-level object discovery tasks.
comparing with SPOT 77. MetaSlotrus trained using only
4.2 Evaluate on Set Prediction
The set prediction task explicitly reveals the effectiveness of each slot in capturing object information. Following this work (46), images from the MS COCO 2017 dataset are encoded into object-centric
7

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Table 1: Object discovery performance with DINOv2 VIT (s/14) for OCL encoding. The input
resolution is 256x 256 224). Tfd, MLP and Dfz are Transformer, MLP, and Diffusion [78 for OCI decoding respectively.
ClevrTex
#slot=11
COCO
#slot=7
VOC
#slot=6
ARI
FG-ARI
mBO
mloU
ARI
FG-ARI
mBO
mloU
ARI
FG-ARI
mBO
mloU
SLATE
17.4
87.4217
44.5
43.3424
17.5g06
28.8203
26.82n3
25.4_03
18.6
26.2aas
37.22n5
36.1
MetaSlotrui
40.9
92.4g07
49.3g44
48.8.14
18.6211
33.5.10
28.2ga7
26.7-0
20.4
30.720
39.0203
37.8a5
DINOSAUR
50.7a341
89,4203
53.3250
52.8
18.2210
35.0212
28.3
26.9-05
21.5407
35.2+11
40.6
39.7.0o
MetaSlotyti
64.6293
89.6:a4
55.2:45
54.5
22.4aa
40.3:05
29.5
27.9-02
32.2+12
43.3gmd
43.9:03
42.1ga2
SlotDiffusion
66.1
#2.7=16
54.3203
53.4
17.7.as
28.7.01
27.0a04
25.6-04
17.0+1a
21.7+11
36.2-09
34.0210
MetaSlotps
81.9
77.6.11
64.2:44
62.8
17.7:02
32.2=07
27.2:02
25.8..1
19.0:a3
24.1:03
36.5
35.3
Table 2: Comparison with SOTA methods: SPOT on MS COCO 2017 (images) and VideoSAUR on YTVIS-HQ (videos). All models use DINOv2 ViT(s/14) backbone. The input resolution is 256 (224x224).
COCO #slot=7
YTVIS(HQ) #slot=7
ARI
FG-RRI
mBO
mloU
ARI
FG-ARI
mBO
mloti
SPOT
20.3
41.1
30.4-01
29.0
VidenSAUR
33.0.04
49.0g09
30.8.04
30.1
MetaSlotyao
23.1
41.2,03
30.5-03
28.6g08
MenSSto-VideoSAURR
60.0=23
67.3.21
33.7
28.3ga7
representations using OCL. Each slot tasked with predicting the object category labels and bounding box coordinates via small MLP. We evaluate classification performance using top-1 accuracy of category labels and assess regression performance using the R2 score of the bounding box coordinates.
Table shows that the proposed MetaSlot (i.e., MetaSlotmip) consistently outperforms the baseline [46] in both object classification and bounding box regression tasks. These results indicate that the object representations captured by MetaSlot are superior, effectively improving the encoding of both categorical and spatial information.
Table 3: Set prediction performance
Table 4: Aggregator comparison.
COCO
class labels
bounding boxes
COCO #slot=7
#slot=7
tuplf
lop21
ARI
FG-ARI
mBO
mloU
DINOSAUR MLP
0.33
0.54
Slot Attention
17.24a2
38.6208
27.7
26.5
MetoSletgaL MLP
0.36
0.56
BO QSA
18.2410
35.0+1.
28.3405
26.9205
MetaSlot
22.4:03
40.3
29.5
27.9-02
4.3 Interpretability Analysis
Gaussian Mixture Model (GMM), where each slot acts as a Gaussian component explaining a subset of pixels. Building on this view, we shift the focus from concrete objects abstract prototypes, positing that the real-world distribution can likewise be factorized into a mixture of such prototypes. Concrete objects in the feature map can then be regarded samples or projections from these prototype distributions. In alot-attention-basee object-centric learning, matching these abstract prototypes manifests as slot initialization, while the iterative attention updates project and refine the prototype distributions onto concrete visual appearances.
Kori et al. [55] interpret Slot Attention (SA)
In SA, all slots sampled from shared Gaussian prior, which lacks object-level inductive cues during initialization. BO-QSA 29 mitigates this limitation by assigning independent Gaussian priors to each slot, thereby promoting greater diversity and improving object-attribute binding. However, its fixed slot count limits its adaptability to the diverse objects encountered in real-world scenes. To address this, proposed MetaSlot introduces set of adaptive prototype slots that capture abstract representations of real-world entities, further enhancing object binding and improving flexibility in complex scenes.
As shown in Fig. 3 MetaSlot's prototype-based initialization yields slots with pronounced semantic consistency and strong object binding-ee c.g., all slots from prototype 268 correspond to "keyboard"

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MetuSlot
BO-QSA
We visualize slot representations initialized from different prototype slots on the COCO dataset, where each column shows specific initialization slot index prottt-pttttype slots in MetaSlot (out of 512) and fixed slots in BO-QSA [29]. MetaSlot's prototype-basd
Figure 3:
initialization yields slots with strong semantic consistency and object binding (e.g., #slot 208 for trucks, #slot 445 for persons). By comparison, the fixed slots in BO-QSA frequently lack such coherent semantic grouping.
objects, while those from prototype 240 correspond to "umbrella" objects. In contrast, BO-QSA, limited by its fixed number of slots, struggles to achieve such fine- tne-grained prototype binding. Addi- tional results on the VOC dataset are reported in Appendix B. Quantitative comparisons under the DINOSAUR (46) decoding framework (Table(4) further confirm that MetaSlot surpasses both SA and BO-QSA on all object discovery metrics, highlighting its superiority in producing disentangled and interpretable slot representations.
4.4 Ablations
To assess the effectiveness of the key architectural components of MetaSlot, perform prehensive ablation study on the MS COCO 2017 dataset. All experiments adopt the DINOSAUR framework with DINOv2 VIT (s/14) encoder and fix the number of slots seven. To validate the contribution individual design choices, we further evaluate two ablated variants: 'MetaSlot w/o noise' omits progressively attenuated noise during slot updates. As shown in Fig 4 injecting noise leads to a lower Adjusted Rand Index (ARI, left) and a higher mean best overlap (mBO, right), implying faster and stable slot aggregation. 'MetaSlot w/o mask' disables the prototype-based masking strategy. Table summarizes performance across three metrics--Foreground ARI (FG-ARI), mean best overlap (mBO), and mean Intersection-over- is pivotal for precise slot-to-object alignment and effective spatial disentanglemennt In addition, Appendix E presents an analysis of the codebook prototype size, where we observe that increasing the number of prototypes yields only marginal improvements in performance. We also compare MetaSlot models with varying slot counts, and the empirically optimal number slots aligns with the long-established consensus within the object-centric learning community. Furthermore, we include additional ablations on architectural components, which provide further evidence for the robustness and effectiveness of our model design.
(mIoU), indicating that each component
5 Conclusion
This paper introduces MetaSlot, a novel aggregator for object-centric learning (OCL) that addresses two long-standing limitations of conventional Slot Attention models: the fixed number of slots and reliance on random initialization. MetaSlot incorporates a global vector-quantized (VQ) prototype codebook alongside two-stage aggregate-and -ndeaa--anndddepliicaeee framework. This design enables the model to adaptively adjust the number of slots based scene complexity and initialize slots

---

Table 5: Ablation study architectural components. Backbone: DINOv2 ViT
Figure 4: Training for MetaSlotMip with/without progressively attenuated noise.
(s/14).
COCO #slot=7
FG-ARI
mBO
mloU
MetaSlot w/o noise
39.4 39,4203
28.9
27.4
MetaSlot w/o mask
38 38.2400
28.5
26.9403
MetaSlot
40.3
29.5
27.9
with semantically meaningful object representations. Extensive experiments show that MetaSlot consistently achieves substantial gains across a range of OCL tasks and offers a robust foundation for future research OCL and its downstream applications.
Limitations and Future Directions
Despite its promising results, MetaSlot still faces several limitations that point to fruitful directions for future research. First, the use of absolute positional encoding inherited from the original Slot Attention makes the model non-equivariant to image translations, potentially causing the codebook prototypes capture rosition-dependen noise patterns. Future work could leverage translation- equivariant mechanisms such as ISA (74) to promote the emergence of consistent,
representations. Second, current object prototypes primarily encode global shape or semantic infor- mation while overlooking finer- tner-grained attribute compositions. Enhancing prototype optimization through compositional generalization that is, constructing compound prototypes integrating multiple attribute-level features- may yield richer and more discriminative object cues for downstream tasks. Third, owing to MetaSlot's two-stage and iterative optimization design, the framework inherently contains self-supervisory signals. Exploring ways identify the semantically complete object slots emerging in the second stage and using them to provide weak supervision for the aggregation process in the first stage could further advance the development of variable-slot object-centric learning. Finally, MetaSlot has yet to be extensively evaluated under out-of-distribution (OOD) conditions. Systematic studies across diverse domains, coupled with efforts to better align the learned code- book prototype distributions with real-world object distributions, could further improve the model's cross-sample and cross-domaie generalization.
Acknowledgments and Disclosure of Funding
We acknowledge the support of Finnish Center for Artificial Intelligence (FCAI), Research Council of Finland flagship program. We thank the Research Council of Finland for funding the projects ADEREHA (grant no. 353198). We also appreciate CSC IT Center for Science, Finland, for granting access to supercomputers Mahti and Puhti, as well LUMI, owned by the European High Performance Computing Joint Undertaking (EuroHPC JU) and hosted by CSC Finland in collaboration with the LUMI consortium. Furthermore, we acknowledge the computational resources provided by the Aalto Science-IT project through the Triton cluster.
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A
Method
Algorithm 1: MetaSlot.
Input: input features input, learnable queries init, number of iterations T Output: object-centric representation 5final
Modules: stop gradient module SG(.), slot attention module SA(::), )), masked slot attention module (SA(-:') vector quantization VQ(.). prune duplicate slots module Prunc(-), update prototype codebook module Update(";)) :), noise injection module Noisy(:..)
# First-Stage :ggregation:
5 Smad &- init. init.detach();
for
do
L [Smid SA (S imputs);
8 S, idæ + VQ(Smid);
Smask: mask (Sune(S, idr);
# Second-Stage :ggregation:
Sfinal t S mask;
12 for t = 1
do
impulnoiss Noisy (impul, l);
Sfinal (SAA(ffmm imput mask);
14
15 Sfinat SG(Sfinal) + init (imitt;);
16 imputnoisy + Noisy(inputs Cimput,T) T);
Sfinal MSA inal impulnoisy, mask);
# Prototype update:
19 USdste()G((Sfmna), mask);
20 return 8final
B Visualization of prototype slots
As shown in Fig. 5 we visualize the refined slots 5final corresponding to the initialization prototype slots on the VOC dataset. The codebook contains 512 object prototypes total. However, since the VOC-trained model is relatively limited in object diversity and scale, many prototype slots receive few or no refined slots assigned to them, making comprchensive visualization infcasible. Therefore, for practical and technical reasons, focus the top 20 most active prototype those associated with the largest number of refined slots in the model trained on the VOC dataset. For each selected prototype slot, randomly sample six refined slots from its assigned set and visualize their corresponding image patches. As shown in the figure, the results clearly demonstrate that the prototype slots exhibit strong concept binding behavior, with refined slots consistently aligned to semantically coherent object categories. These findings are consistent with our theoretical expectations regarding the semantic consistency induced by prototype-guide -uiiddd initialization.
as
C Experimental Details
Implementation and Reproducibility. To ensure fair comparison, we re-implemented all baseline models from scratch rather than relying on publicly reported results. Throughout all experiments, we kept data augmentation strategies, the visual feed-forward module (VFM) the OCL encoder-baseed on DINOv2 VIT-s/14 (50] and all training hyperparameters identical to those reported in the original papers. Furthermore, we replaced each model's 's original variational autoencoder (VAE) component with large-scale pre-traine me-ttraieed TAESD module [78), which is based Stable Diffusion.
16

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72
328
VOC
VOC
VOC
VOC
VOC
VOC
130
352
291
298
VOC
YOC
VOC
VOC
VOC
Figure Visualize slot representations initialized from different prototype slots on the VOC dataset.
All models-including both the MetaSlot-augmented variants and their respective baselines-wee were trained using the Adam optimizer 77 on single NVIDIA V100 GPU with 16-bit mixed precision. Each run consisted of 50000 training steps, with batch size of 32 and four data-loading workers. We set the initial learning rate the codebook size was fixed at 512, the feature map resolution at 256 x 256, and the embedding dimension 256. The number of slots for each model remained consistent with its original baseline configuration. To reduce the impact of randomness, all experiments were repeated with three different random seeds, and we report the mean results.
10 and maintained it throughout training. For the MetaSlot module,
Pre-trained VQ-VAE Configuration for SLATE and Slot Diffusion. For the SLATE and Slot Diffusion baselines, we adhered to the standard VQ- (Q-VAE implementation based on ResNet-18 18. Specifically, we used codebook size of 4096 and an embedding dimension of 256. Pre-training was conducted for 30000 steps with batch size of shared between the text and vision branches, four data-loading workers, and an initial learning rate of 2 x 10-3, The feature map resolution remained 256 x 256. As before, each configuration was evaluated over three independent runs, and the reported metrics represent the averaged performance across these trials.
D Supplementary Ablation Experiments
Impact Codebook Size As shown in table we investigated the impact of different codebook sizes (256, 512, and 1024) on model performance. We observed that codebook size has limited effect on performance; however, a larger prototype set allows for finer distinctions between slots that share semantic concepts but differ in spatial location. For example, shown in Fig. 3 slot #445 corresponds to the concept "person" located on the right side of the image, while slot #337 represents the same concept but appears at the center of the scene.
Impact of Slot Number shown table we examined how the number slots affects final performance. Since the codebook and the aggregator module are jointly optimized, the quality of the
17

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Table 6: Results of MetaSlot with varying codebook sizes on MS COCO.
DINOSAUR on MS COCO
ARI
FG-ARI
mBO
mloU
MctaSlot, codehook -ittroooksiice26666----
26.7
38.1
29.4
27.5
MetaSlot, codehonk srcchooksszc=512
22.4
40.3
29.5
27.9
MetaSlot, codehonk codehonk_size=1104
20.0
40.4
29.2
27.3
Table 7: Results of MetaSlot with varying slot numbers on MS COCO.
DINOSAUR on MS COCO
ARI
FG-ARI
mBO
mloU
MetaSlot, slot num=5
25.5
37.8
29.3
27.4
MetaSlot, slot num=?
22.4
40.3
29.5
27.9
MetaSlot, slot num=11
19.8
38.7
28.4
26.9
MetaSlot, slot num=15
15.9
36.0
27.2
26.0
aggregator- segregaoor-pariicllar, in early training stages can significantly influence codebook convergence. We found that the empirically optimal slot count aligns well with long-standing choices commonly adopted in the community.
Impact of Architectural Components As shown in table 8 we conducted additional experiments to gain deeper insight into the working mechanism of MetaSlot. Specifically, tested two variants: (1) removing the prototype guidance and instead initializing slots in the second stage by sampling from separate Gaussian distributions, done in the first stage; and (2) disabling the reactivation mechanism for stale (dead) prototypes during the codebook update process. The results confirm the effectiveness of our module design and are consistent with the theoretical assumptions proposed in the paper.
Furthermore, prior studies in object-centric learning have consistently shown that simply increasing the number of Slot Attention iterations does not lead to meaningful performance gains. To validate this observation, we performed additional ablation study the BO-QSA model by increasing its iteration count to 6, matching that of MetaSlot. As shown in the results, increasing the iteration count alone does not improve BO-QSA's performance, further highlighting the importance of our design choices beyond iteration depth.
Table 8: Supplementary ablation chitectural components.
Table 9: Comparison with SlotContrast the
MOVi-C dataset.
MS COCO #slot=7
Method
FG-ARI
mBO
FG-ARI
mBO
mloU
SlotContrast
62.4
30.6
MetaSlot w/o proto
40.2
29.1
27.6
MetaSlot
63.9
35.0
MetaSlot w/n prune
MetaSlot
40.3
29.5
27.9
BO-QSA.iter_munne nom
37.9
27.7
26.3
E
Supplementary Comparative Experiments
Comparative Evaluation against SlotContrast As shown in table 10 we further include comparison with the SlotContrast(76 model on the MOVi-C dataset. In these experiments, MetaSlot does not employ SlotContrast losses or any temporal-specific enhancements. Due to constraints in time and computational resources, we used a batch size of 8 (vs. 64 in SlotContrast), 24 frames per sample, and trained for up to 20,000 steps (vs. 100,000 in SlotContrast). Despite these limitations, MetaSlot still demonstrates notable performance advantages.
It is worth noting that SlotContrast's contrastive loss was originally designed for fixed number of slots, and extending to handle variable slot counts would require additional effort. Nevertheless, discussed earlier, without incorporating SlotContrast's core contribution- contrastive
18

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loss MessSltaloo achieves comparable or even superior performance in object discovery tasks. We believe that integrating SlotContrast's contrastive learning objective in the first stage with MetaSlot's dynamic aggregation in the second stage could further enhance performance, particularly for temporal object discovery. However, this would necessitate architectural modifications that are beyond the scope of the current work and are left for future research.
Table 10: Comparison with SlotContrast on the MOVi-C dataset.
Table 11: Comparison with AdaSlot the MS
COCO dataset.
Method
FG-ARI
mBO
Method
FG-ARI
mBO
SlotContrast
62.4
30.6
AdaSlot
35.6
29.4
MetaSlot
63.9
35.0
BO-QSA
35.0
28.3
MetaSlot
40.3
29.5
Comparative Evaluation against AdaSlot As shown Table 11 we further report the results of AdaSlot[53] compared with MetaSlot. All models use DINOv2 ViT-S/14 backbone, and the input resolution is 256x256 (or 224x224). We speculate that the performance difference is partly due to the fact that, when the number of slots varies, the model can no longer apply separate Gaussian initialization to each slot individually.
Additional Evaluation of VQ-based Object-Centric Methods We further evaluated several object-centric learning methods that employ vector quantization. In SysBinder|651 and (666) the guantization mechanisms operate the attribute level within each slot. However, this design does not necessarily lead to improved representation quality in unsupervised settings. In synthetic datasets, object attributes such color, size, shape, and material relatively fixed and often be described using fewer than ten shared labels. contrast, real-world datasets such as COCO and VOC contain objects with far more diverse and non-shared attributes. For instance, the object person cannot be meaningfully described with the same attribute set as the object mouse. This discrepancy likely explains why SysBinder and NLoTM were not originally evaluated real-world benchmarks.
Furthermore, the initial intuition behind MetaSlot is inspired by Platonic philosophy. In Plato's theory of Forms, sensible particulars are intelligible only insofar they "participate" (metechē) in transcendent Form (Phaedo 100c-d). Similarly, Aristotle maintains in the Categories that accidents belong to substances only predicates of discourse, not essential ingredients shared by all beings (Categories 2-11-
Our empirical evaluation on COCO and ClevrTex shows that MetaSlot substantially outperforms both SysBinder and NLoTM. Although SysBinder achieves slight advantage in FG-ARI on ClevrTex, underperforms the DINOSAUR baseline on other key metrics such ARI, mBO, and mloU. More- over, the SysBinder paper only reports FG-ARI and does not include ARI, mBO, or mloU-metrics widely regarded as standard for evaluating object discovery. All models DINOv2 ViT-S/14 backbone, and the input resolution is 256x256 (or 224x224)..
Table 12: Comparative results of Sys- Binder, NLoTM, DINOSAUR, and MetaSlot on MS COCO.
Table 13: Comparative results of SysBinder, NLoTM, DINOSAUR, and MetaSlot on ClevrTex.
ClevrTex
MS COCO
ARI
FG-ARI
mBO
mloU
PG-ARI
mBO
mloU
SysBinder NLoTM
21.7
91.4
46.8
46.5
SysBinder
16.8
37.7
27.2
26.0
21.5
88.7
44.1
43.1
NLoTM
57.8
14.7
23.6
17.8
DINOSAUR
50.7
89.4
53.3
52.8
DINOSAUR
18.2
35.0
28.3
26.9
MetaSlot
64.6
89.6
55.2
54.5
MetaSlot
22.4
40.3
29.5
27.9
19

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Reviews:
[{"review_id": "nvDDucidvD", "reviewer": "Reviewer_MgVn", "summary": "The paper proposes to use a set of prototypes for making SA-based OCL models (including real-world ones such as DINOSAUR, VideoSAUR, and SPOT), more flexible in terms of the number of slots and not restricted by random initialization that puts all the burden on iterative SA decomposition of the scene. It shows nice improvements for Video-based OCL that also requires slots to be consistent in time. To achieve this, the paper employs a two-step procedure: applying the same slot attention module to random slots and then to masked discrete prototypes, which are obtained as a batch k-means over slots. To align the second state of SA, the paper is adding decreasing noise to the inputs.", "questions": "I believe that the proposed approach is a simple yet meaningful way to incorporate flexibility in terms of the number of slots into OCL.  My primary concern is that the presentation and experimental analysis could be improved, making it more helpful to the community. If the paper can address most of the weaknesses mentioned, I would be happy to increase my score.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths: \n\n1. The paper is generally well-written and well-structured. \n2. The paper evaluates the quality of the slots on different downstream tasks, not only scene decomposition, but also the content of the features and not only the quality of the slots. \n3. Visualization of the meta slots (Figure 3) is nice for understanding the reasons for the method's performance improvements. \n\n### Weaknesses:\n1. Some presentations still can be improved. For example, if I understand correctly, the Masked Slot Attention (without noise) is basically equivalent to running fewer slots as inputs, while a masking is needed to make this procedure efficient for batch processing. Is this a case? If yes, the presentation could be improved accordingly. If no, it would be great to discuss or note what the difference is.  Also the name is a bit misleading given the current analysis: from the name it seems like paper tried to solve only one issue of the number of slots, while during reading it becomes clear that it is tacking both interesting and flexible semantic initialization and also number of slots issue. \n\nAdditionally, there are some related works that are missing, such as SlotContrast [1], which observed automatic adaptation to the number of slots due to a contrastive objective and achieved a state-of-the-art result in video object-centric learning on YT-VIS.\n\n2. Although some ablations are performed, they are still insufficient to fully understand the performance improvements.  So I suggest some additional ablations: what if we use random init in the second iteration and just change the number of slots (e.g use masked SA with masks for slots that were mapped to the same prototype). Another ablation could be if we are not removing outdated prototypes (Eq 11.) \n3. While the main point of MetaSlot is to improve any OCL method, it is still important to know if it is possible to improve state-of-the-art OCL learning. For example, it would be beneficial to compare MetaSlot with SlotContrast [1] to determine if MetaSlot further enhances the contrastive slots and masks of SlotContrast. Especially, it is important that the comparison with SPOT is a bit mixed. While authors showed that MetaSlot+one Stage SPOT is comparable with two-stage SPOT, they didn't show that they can improve upon two-stage SLOT by adding MetaSlot. \n4. Number of slots analysis: Direct analysis of the number of slots per image vs GT number of objects is missing (similar to AdaSlot). Although their analysis is somewhat limited due to the number of background slots, it remains quite informative. Additionally, the paper would benefit from a sensitivity analysis (e.g., by varying the maximum number of slots used or the number of prototypes used). Would we observe that more slots are turned off, and that the results are still similar, or would we still oversegment? \n\n[1] Manasyan A, et al. Temporally Consistent Object-Centric Learning by Contrasting Slots, CVPR 2025", "was_revised": true, "final_justification": "After reading the answers from the rebuttal, I think the authors addressed my main concerns and I'm happy to increase my score towards acceptance. \n\n\"Due to limitations in time and computational resources...\"\nIf accepted, I encourage authors to run full experiments for camera ready, not a shorter one, to make sure that the comparison with other methods are valid.", "rebuttal": "We appreciate the reviewer’s constructive feedback and address the concerns as follows.\n\n>The Masked Slot Attention (without noise) is basically equivalent to running fewer slots as inputs, while a masking is needed to make this procedure efficient for batch processing. Is this a case? \n\nYes, your understanding is absolutely correct. In MetaSlot, the Slot Attention (SA) used in the first stage and the Masked Slot Attention (MSA) employed in the second stage share exactly the same parameters. Therefore, they are two consecutive calls to the same SA module rather than two independent aggregators. Consequently, we keep the same upper bound on the number of slots in the MSA stage and rely on binary masks to deactivate redundant slots, thereby enabling variable slot counts while preserving batch‑level efficiency on the GPU.\n\n>From the name it seems like paper tried to solve only one issue of the number of slots, while during reading it becomes clear that it is tacking both interesting and flexible semantic initialization and also number of slots issue.\n\nThank you for recognizing the MetaSlot mechanism! Regarding the choice of our paper's title, we emphasized “variable number of slots” because a fixed number of slots has long been considered a core bottleneck in slot-based unsupervised learning, with very limited existing solutions. To date, the only method that truly attempts dynamic slot allocation is AdaSlot. However, its performance on real-world datasets (such as COCO and VOC) in terms of MBO, ARI, and FG ARI metrics not only fails to surpass that of fixed-slot Slot Attention (SA), but in fact, performs worse.\n\nWe speculate that this is partly because, when the number of slots varies, the model can no longer apply the Gaussian initialization provided by BO-QSA to each slot individually. As a result, the initial binding between semantic and positional information is weakened, negatively affecting performance. Additionally, AdaSlot predicts object count directly from the feature map, which makes it highly sensitive to the weight of the regularization loss.\n\nIn contrast, MetaSlot employs a two-stage aggregation strategy. Its mask indices are derived from codebook matching, ensuring that the slot layout in the second stage remains consistent with the Slot Attention output from the first stage. This prevents confusion in slot-object binding. Given this landscape, we argue that achieving substantial improvements while **supporting a variable number of slots—without compromising performance**—represents a more impactful contribution to the field.\n\n>There are some related works that are missing, such as SlotContrast.\n\nWe conducted video object-centric learning experiments on YT-VIS primarily to demonstrate the applicability of MetaSlot in video settings. It is important to note that the current version of MetaSlot has not been specifically designed with additional temporal modeling for video scenarios. \nWe have added experimental results where MetaSlot serves as the grouper in the SlotContrast evaluation framework, evaluated on the MOVi-C dataset. In these experiments, MetaSlot does not utilize SlotContrast losses or any temporal-specific enhancements. Due to limitations in time and computational resources, we used a batch size of 8 (compared to 64 in SlotContrast), 24 frames per sample, and trained for a maximum of 20,000 steps (compared to 100,000 in SlotContrast). MetaSlot still demonstrates significant performance advantages. We will include the full comparison experiments in the appendix of the revised version. The parameters will be aligned with those used in SlotContrast to ensure the experiments can be thoroughly validated by the community.\n\n| MOVi-C         | FG-ARI | MBO  |\n|----------------|--------|------|\n| SlotContrast | 62.4 | 30.6   |\n| MetaSlot       | 63.9   | 35.0 |\n\n>Although some ablations are performed, they are still insufficient to fully understand the performance improvements. \n\nIn our previous ablation studies, we examined the impact of MetaSlot’s masking mechanism and noise injection mechanism, demonstrating that appropriate slot assignment can significantly enhance model performance. Moreover, even when the masking mechanism is removed, MetaSlot still outperforms the original Slot Attention, which we believe provides indirect evidence of the effectiveness of the prototype-guided mechanism.\n\nWe also added two new experiments to gain deeper insight into the working mechanism of MetaSlot:\n(1) Removing the prototype guidance and instead initializing slots in the second stage by sampling from separate Gaussian distributions, as done in the first stage;\n(2) Disabling the reactivation mechanism for stale(dead) prototypes during the codebook update process.\n\nThese results further support the effectiveness of our module design and align with the theoretical assumptions proposed in the paper.\n\n| Setting         | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| MetaSlot (full) | 22.4 | 40.3   | 29.5 | 27.9 |\n| w/o proto       | 21.1 | 40.2   | 29.1 | 27.6 |\n| w/o prune       | 21.7 | 40.0   | 29.1 | 27.5 |\n\n>While the main point of MetaSlot is to improve any OCL method, it is still important to know if it is possible to improve state-of-the-art OCL learning.\n\nTo objectively compare the performance of different methods, we adopted a unified, custom-built evaluation framework (We will release the evaluation framework as open source in the future.). It is worth noting that Spot's two-stage distillation strategy is highly sensitive to hyperparameters and often requires additional tuning, which is beyond the scope of what MetaSlot aims to address. For example, SPOT applies gradient clipping using the infinity norm with a maximum value of 0.3, whereas most mainstream OCL methods use the L2 norm with a maximum value of 1. In our experiments integrating various encoder-decoder architectures—including VQ-VAE, DVAE, diffusion models, and Transformers—MetaSlot consistently achieved significant and stable performance improvements, further validating its effectiveness and broad applicability.\n\nAdditionally, SlotContrast’s contrastive loss was originally designed to work with a fixed number of slots, and extending it to support variable slot counts would require additional effort. However, as previously discussed, even without incorporating SlotContrast’s core contribution—the contrastive loss—MetaSlot demonstrates comparable or even superior performance in object discovery tasks. We believe that combining SlotContrast’s contrastive learning objective in the first stage with MetaSlot’s dynamic aggregation in the second stage could yield further improvements, particularly for temporal object discovery tasks. Nonetheless, this would entail architectural modifications that fall outside the scope of the current work and will be explored in future research.\n\nIn summary, our core contribution is the proposal of MetaSlot, a general-purpose variant of Slot Attention. Further optimizations for temporal modeling can be explored in future work, following the evolution path of VideoSaur from DINOSAUR or SAVi from the original Slot Attention.\n\n>Direct analysis of the number of slots per image vs GT number of objects is missing. The paper would benefit from a sensitivity analysis.\n\nWe analyzed the number of objects discovered by MetaSlot on the COCO dataset. Given the maximum slot count was capped at 7, the resulting distribution exhibited a skewed shape with a mean of 4.9, a standard deviation of 1.2, and a range from 1 to 7. In contrast, the ground-truth object count in COCO follows a long-tailed distribution—with a mean of 6.3 (elevated due to the long-tail nature of the data), a standard deviation of 6.4, and a range from 0 to 63. Visualization results show that MetaSlot approximates the real object count distribution curve reasonably well, despite the capped slot limit. Additionally, we have included comparative experiments with AdaSlot, and the results remain consistent with the statements in the main paper.\n\nFurthermore, we investigated the impact of different codebook sizes (256, 512, and 1024) on model performance. We observed that codebook size has a limited effect on performance; however, a larger prototype set allows for finer distinctions between slots that share semantic concepts but differ in spatial location. For example, as shown in Figure 3, slot #445 corresponds to the concept of “person” located on the right side of the image, while slot #337 represents the same concept but appears at the center of the scene.\n\nWe also examined how the number of slots affects final performance. Since the codebook and the aggregator module are jointly optimized, the quality of the aggregator—particularly in early training stages—can significantly influence codebook convergence. We found that the empirically optimal slot count aligns well with long-standing choices commonly adopted in the community.\n\nAll of the above analyses and results will be included in the appendix of the revised paper, where we provide a more detailed discussion on MetaSlot’s capability in estimating object counts, along with extended comparative experiments and supporting analyses.\n\n| DINOSAUR on MS COCO         | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| MetaSlot, codebook_size=256 | 26.7 | 38.1   | 29.4 | 27.5 |\n| MetaSlot, codebook_size=512 | 22.4 | 40.3   | 29.5 | 27.9 |\n| MetaSlot, codebook_size=1024| 20.0 | 40.4   | 29.2 | 27.3 |\n| MetaSlot, slot_num=5 | 25.5 | 37.8   | 29.3 | 27.4 |\n| MetaSlot, slot_num=7 | 22.4 | 40.3   | 29.5 | 27.9 |\n| MetaSlot, slot_num=11| 19.8 | 38.7   | 28.4 | 26.9 |\n| MetaSlot, slot_num=15| 15.9 | 36.0   | 27.2 | 26.0 |\n\n| DINOSAUR on MS COCO   | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| AdaSlot | 17.8 | 35.1   | 27.9 | 26.8 |\n| MetaSlot | 22.4 | 40.3   | 29.5 | 27.9 |"}, {"review_id": "xAX5VHM6Uw", "reviewer": "Reviewer_Y71X", "summary": "The paper proposes MetaSlot, a Slot Attention variant for object-centric learning that enables dynamic slot allocation through a vector-quantized (VQ) codebook of object prototypes. The method introduces a two-stage aggregation process: an initial stage aligns slots with prototypes and prunes duplicates, while a second refinement stage injects progressively annealed noise and uses masked attention.", "questions": "\"Optimal\" slot number: Can the authors provide a more principled discussion of what constitutes an optimal number of slots? Or at least clarify how MetaSlot approximates this adaptively? A more thorough discussion of codebook dynamics, computational trade-offs, and theoretical understanding of slot allocation would strengthen the paper.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "1. Strengths:\n- Tackles a real limitation of traditional Slot Attention by removing the constraint on fixed slot numbers.\n- Uses a plug-and-play design that can be integrated with multiple object-centric frameworks (Transformer, MLP, diffusion).\n- Empirical results show consistent improvements in segmentation and representation quality.\n\n2. Weaknesses:\n- Limited originality: The method is largely a recombination of known techniques (VQ, masking, noise annealing) applied to Slot Attention. The dynamic slot count is driven by prototype quantization, which is conceptually straightforward.\n- Overstated claims: Several statements (e.g., line 24–25) present strong assertions about the necessity of discrete object-level representations without theoretical or empirical support. Recent large vision–language models (e.g., GPT-4) achieve strong zero-shot reasoning without an explicit object slot bottleneck. Their success shows that such reasoning can also emerge from dense, distributed features, so the claim should be softened or empirically justified.\n- No theoretical analysis: The paper lacks discussion or formal insight into when dynamic slot counts are beneficial, or what constitutes an \"optimal\" slot number. Over-segmentation is assumed to be harmful without deeper justification.\n- No discussion of computational cost: The two-stage aggregation and codebook updates introduce overhead, but FLOPs or throughput are not reported. Performance gains may be partially attributable to increased compute.\n- Codebook sensitivity unexplored: The size of the prototype codebook (fixed to 512) is not studied, despite being critical to slot granularity. It would be helpful to conduct an ablation on the number of prototypes and provide insight into how it affects the number and granularity of resulting slots—e.g., do more prototypes lead to finer, potentially redundant slots, while fewer prototypes yield coarser but more stable object groupings? This is important for understanding and controlling the behavior of MetaSlot.\n- Writing clarity issues: Some equations (e.g., Eq. (1)) are needlessly complicated, and some passages are vague or imprecise.", "was_revised": true, "final_justification": "The rebuttal satisfactorily addresses certain concerns and partially mitigates the issues I raised. Nonetheless, important questions remain regarding the work’s originality, the absence of rigorous theoretical analysis, and ambiguities in the reported computational costs. Considering the partial resolution of my concerns, as well as the generally positive assessments from other reviewers, I will modestly increase my score.", "rebuttal": "We appreciate the reviewer’s constructive feedback and address the concerns as follows.\n\n>Limited originality\n\nTo the best of our knowledge, we are the first to leverage vector quantization (VQ) to learn more expressive query slots in slot-based models, whereas existing approaches typically rely on sample-independent Gaussian priors. We are also the first to introduce a noise annealing strategy to improve the iterative dynamics of Slot Attention. Furthermore, we are the third to implement object-centric learning with a variable number of slots (following SOLV and AdaSlot), while there are nearly a thousand papers based on Slot Attention that still rely on a fixed number of slots. Among them, SOLV adopts a post-hoc clustering method rather than end-to-end learning and requires careful hyperparameter tuning. AdaSlot, despite introducing dynamic slot counts, exhibits a slight performance drop compared to its fixed-slot counterpart. In contrast, we provide comparative experiments showing that MetaSlot achieves substantial performance gains over AdaSlot.\n\nTherefore, we argue that achieving substantial improvements while supporting **a variable number of slots—without compromising performance**—represents a more impactful contribution to the field.\n\nMoreover, in slot-based object-centric learning (OCL), introducing a quantization module to enhance the Slot Attention mechanism is neither straightforward nor intuitive. This is primarily because such methods are inherently unsupervised and lack any form of labeled data, often resulting in suboptimal or insufficient slot representations. Additionally, the semantic information captured by Slot Attention during early training stages is usually ambiguous. Using these ambiguous representations to update the codebook, which in turn influences Slot Attention, can easily lead to mode collapse. Therfore, MetaSlot’s ability to successfully incorporate vector quantization, provide more effective prior guidance, and support dynamic slot adaptation is the result of deliberate and careful design. This includes key components such as Mask Slot Attention, Noise Injection, and the Two-step Aggregation mechanism.\n\nIt is also worth noting that the vector quantization strategy adopted in MetaSlot is fundamentally different from those used in existing OCL models. Due to space limitations, we respectfully refer the reviewer to our response to Reviewer gev3 for a more detailed discussion on vector quantization. We will also include a comprehensive elaboration on object-centricity, vector quantization, and slot-based OCL in the appendix of the revised version.\n\n| DINOv2 ViT-S/14 on MS COCO | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| AdaSlot | 17.8 | 35.1   | 27.9 | 26.8 |\n| MetaSlot | 22.4 | 40.3   | 29.5 | 27.9 |\n\n>Overstated claims\n\nThank you for your valuable feedback. We will revise our original discussion and incorporate additional relevant academic works to further support our claims and ensure the rigor of our arguments.\n\nFirstly, a core feature of high-level cognition is widely considered to be the ability to systematically manipulate and compose discrete units of knowledge [1]. While structured representations naturally emerge as tokens in text, obtaining analogous discrete representations in unstructured modalities like images remains a fundamental challenge [2]. This limits current generative models in understanding fine-grained object interactions and modeling physical relationships. As such, Object-Centric Learning (OCL) primarily focuses on the upstream challenge of representation construction, rather than directly addressing downstream tasks involving complex reasoning.\n\nSecondly, recent advances in multimodal foundation models have demonstrated that object-centric representations can significantly enhance model understanding [3, 4].  Moreover, a substantial body of empirical research in robotic manipulation has shown that adopting discrete, object-oriented visual representations greatly enhances a robot’s ability to perceive, plan, and act in cluttered and dynamic environments[5].\n\n>No theoretical analysis\n\nThis direction currently lacks a well-established theoretical framework, making theoretical analysis challenging; we plan to explore this in future work. However, regarding the impact of slot count on performance, we can provide a more in-depth experimental analysis as follows. Since the codebook and the aggregator module are jointly optimized, the performance of the aggregator can influence the convergence of the codebook, especially during the early stages of training. Therefore, we  found that the optimal slot count aligns with the long-standing empirical choices commonly adopted by the community.\n\n| DINOSAUR on MS COCO   | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| MetaSlot, slot_num=5 | 25.5 | 37.8   | 29.3 | 27.4 |\n| MetaSlot, slot_num=7 | 22.4 | 40.3   | 29.5 | 27.9 |\n| MetaSlot, slot_num=11| 19.8 | 38.7   | 28.4 | 26.9 |\n| MetaSlot, slot_num=15| 15.9 | 36.0   | 27.2 | 26.0 |\n\n>No discussion of computational cost\n\nRegarding the concern about computational cost, we report a detailed comparison between MetaSlot and the original Slot Attention in the table below:\n\n| Model          | Parameters (M) | FLOPs (G) | GPU Memory (MB) |\n| -------------- | -------------- | --------- | -------------- \n| MetaSlot       | 33.1           | 44.2      |558\n| Slot Attention | 33.0           | 43.9      |554\n\nThe difference between the two is minimal (less than 1%), indicating that the significant performance gains of MetaSlot are not due to increased computational resources, but rather stem from our proposed structural innovations—such as Mask Slot Attention and the two-step aggregation mechanism. \n\nIn addition, we note that previous literature in object-centric learning has consistently shown that merely increasing the number of Slot Attention iterations does not yield meaningful performance improvements. To further verify this, we conducted an additional ablation study on the BO-QSA model, increasing its iteration count to 6 to match that of MetaSlot. The results clearly show that increasing the iteration count alone did not improve BO-QSA’s performance.\n\n| DINOSAUR on MS COCO   | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| MetaSlot | 22.4 | 40.3   | 29.5 | 27.9 |\n| BO-QSA, iter_num=6 | 17.1 | 37.9 | 27.7 | 26.3 |\n\n>Codebook sensitivity unexplored\n\nWe conducted an in-depth exploration and analysis of different codebook sizes, including 256, 512, and 1024. Based on experimental results and visualizations, we observed that a smaller codebook size (e.g., 256) tends to cause slight under-segmentation, as reflected by a marginal improvement in ARI but a slight drop in FG-ARI. When increasing the size to 1024, we found that ARI slightly decreased while FG-ARI improved compared to the 512-sized version. This is because **the number of prototype slots (256, 512, 1024) is significantly smaller than the total number of objects in the dataset** (about 2.5 million). As a result, **increasing the number of prototypes does not lead to severe over-segmentation, and all variants in this ablation study still outperform the original Slot Attention by a clear margin.**\n\nMoreover, we observed that a larger codebook (e.g., size 1024) allows the model to distinguish between spatially distinct but semantically similar slots more precisely. For example, one prototype may specialize in representing horses in the upper-left region of an image, while another captures horses in the lower-left region. A larger codebook also enables finer-grained object categorization, such as further differentiating dials into circular and square types. However, it may also introduce minor over-segmentation effects, such as isolating the head of a person into a separate slot. That said, this level of over-segmentation is generally acceptable in practical applications, as the model still maintains strong semantic consistency across finer divisions—unlike forced over-segmentation caused by severe mismatch between object and slot numbers.\n\nThis phenomenon also reflects a broader challenge in the object-centric learning community: the difficulty of clearly defining what constitutes an \"object.\" However, this issue lies beyond the scope of our current MetaSlot paper.\n\n| DINOSAUR on MS COCO         | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| MetaSlot, codebook_size=256 | 26.7 | 38.1   | 29.4 | 27.5 |\n| MetaSlot, codebook_size=512 | 22.4 | 40.3   | 29.5 | 27.9 |\n| MetaSlot, codebook_size=1024| 20.0 | 40.4   | 29.2 | 27.3 |\n\n>Writing clarity issues\n\nThank you for raising the concern regarding writing clarity. Most of the equations in our paper follow conventions commonly adopted in the object-centric learning community—for instance, Equation (1) is consistent with the Slot Attention formulation used in BO-QSA. That said, we sincerely welcome any suggestions for improvement. If you could kindly specify which equations or sections felt unclear, we would be more than happy to revise them to improve clarity and readability.\n\n[1] Lake, Brenden M., et al. Building machines that learn and think like people, Behavioral and brain sciences 40 2017\n\n[2] Greff, Klaus, Sjoerd Van Steenkiste, and Jürgen Schmidhuber. On the Binding Problem in Artificial Neural Networks, preprint 2020\n\n[3] Wang, Shihao, et al. Omnidrive: A holistic llm-agent framework for autonomous driving with 3d perception, reasoning and planning. CoRR 2024.\n\n[4] Zhang, Tao, et al. Omg-llava: Bridging image-level, object-level, pixel-level reasoning and understanding, NeurIPS 2024\n\n[5] Shi, Junyao, et al. Composing Pre-Trained Object-Centric Representations for Robotics From “What” and “Where” Foundation Models, ICRA 2024"}, {"review_id": "oojBzqOTwB", "reviewer": "Reviewer_ayFM", "summary": "The paper proposes an object-centric learning method named MetaSlot.\nThe method leverages two slot attention modules separated by a vector quantization step that serves as a deduplication step for the initial slots, effectively allowing to adapt the number of slots to the image.\nThe method is evaluated on several datasets, including COCO, Pascal VOC, and ClevrTex, and compared against several baselines, such as SlotDiffusion, SLATE, and DINOSAUR.", "questions": "In figure 2, the predicted masks tend to be bigger than the enclosed objects, for example around the people and the cows.\nIs this artifact a result of the training procedure? Can it be reduced? How can the reported scores be so high if the masks are so imprecise?\n\nAs it stands, the method makes use of two latent clustering methods: the iterative slot attention, which is similar to soft clustering, and vector quantization, which also clusters the slots from phase 1 into a fixed number of prototypes for deduplication.\nWith this in mind, can we simply see object-centric methods as a form of clustering? And can this perspective help us simplify existing methods rather than adding complexity?\nA discussion on this would be welcome and it could help re-center the contribution of the paper.\n\nMinor points:\n- There is some repetition in the first two paragraphs of section 2.3, specifically lines 127-133 and 134-142. I recommend revising the text and consolidating these paragraphs.\n- What is the difference between the proposed \"masked slot attention\" and simply dropping slots that are not used?\n  The paper described the \"masked slot attention\" as a novel contribution, but it does not seem too significant.\n- L304: typo \"mean boundary overlap\" -> \"mean best overlap\".", "limitations": "The authors provide a discussion about the limitation of their work in the appendix.\nWhile I agree with most points, I highlight that the method is only tested on a single backbone, DINOv2 ViT-S, and that the experiments in section 4.2 are even more limited, as they include a single dataset and a single baseline.\nGiven the variability in performance across datasets, tasks, metrics, backbones, and object-centric methods, the results are not as significant as they could be.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "The paper reports positive improvements across various datasets and metrics, demonstrating the effectiveness of the proposed method.\nThe re-implementation and re-evaluation of several baselines, such as DINOSAUR, SLATE and SlotDiffusion, is a a valuable contribution, though the execution is not without issues, as discussed below.\n\nThe main weakness is the limited experimental setup, despite it being deemed \"extensive\" in the text.\n- Missing comparison with AdaSlot, which is mentioned in the related work section as the most prominent method to obtain an adaptive number of slots. Considering the focus of the paper, this is a significant omission.\n- In the literature on object-centric methods, it is common to include the MOVI-C and MOVI-E datasets in the evaluation. For example, the cited baselines SPOT and SlotDiffusion include these datasets in their evaluation. While I agree that datasets such as CLEVR and ShapeStacks can be left out because of their simplicity, I think the MOVI datasets should be part of the evaluation.\n- Only DINOv2 ViT-S is used as the backbone, leaving out larger backbones and alternative models such as CLIP or MAE. This choice reduces the significance of the results, as it is not clear whether the proposed method would work with other backbones. Also note that the cited baselines use larger backbones, typically ViT-B/16 or ViT-S/8, so the results are not directly comparable. It would be good to demonstrate that the re-implementation of the baselines matches the original results.\n- If space is a concern, reporting only one of mIoU or mBO in the main text would a reasonable compromise to include more datasets and baselines.\n\nA minor issue regarding clarity: in tables 1-4, results are reported with a confidence interval. For many results, the confidence intervals of MetaSlot and the baseline overlap. In those cases, I do not think it's appropriate to mark the MetaSlot numbers in bold, as if to suggest a significant improvement.\n\nIn terms of originality, the proposed method combines existing techniques, such as slot attention and vector quantization/vocabulary learning. The experimental results are positive, but the method does not introduce a fundamentally new approach, nor it provides new theoretical insights on the learning mechanisms of object-centric methods. If the main contribution is the ability to automatically adapt the number of slots for each image, a more thorough evaluation of this aspect is needed. For example, comparing with AdaSlot in terms of performance, number of \"discovered\" objects, and characterization of the learned prototypes. An insightful research question could be: \"for years, we have used 7 slots for COCO, 11 for CLEVR, and 6 for VOC, but how optimal is this choice?\".", "was_revised": true, "final_justification": "My initial score was already positive and the rebuttal addressed my concerns, therefore I recommend acceptance.", "rebuttal": "We appreciate the reviewer’s constructive feedback and address the concerns as follows.\n\n>Missing comparison with AdaSlot, which is mentioned in the related work section as the most prominent method to obtain an adaptive number of slots.\n\nThank you for the insightful suggestion regarding the comparison with AdaSlot. We did not include it in our original experimental design, as AdaSlot does not exhibit clear advantages over fixed-slot Slot Attention on real-world datasets, and in some cases even shows degraded performance. We have included a comparative experiment between MetaSlot and AdaSlot. The results align well with our findings and further support the claims made in the paper. Moreover, we report the results of the MetaSlot model using ViT-B/16 under the same training settings as in the AdaSlot paper. Due to limitations in time and computational resources, MetaSlot was trained for only 40,000 steps (compared to 500,000 steps in AdaSlot), yet it already surpasses the best performance reported in the AdaSlot paper.\n\nWe speculate that this is partly because, when the number of slots varies, the model can no longer apply separate Gaussian initialization to each slot individually. In contrast, MetaSlot adopts a two-stage aggregation strategy that ensures the slot configuration in the second stage remains aligned with the Slot Attention output from the first stage. Therefore, we argue that **enabling variable slot numbers without sacrificing performance** is a more impactful contribution. We will incorporate the above discussion into the revised version of the paper.\n\n| DINOv2 ViT-S/14 on MS COCO | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| AdaSlot | 17.8 | 35.1   | 27.9 | 26.8 |\n| MetaSlot | 22.4 | 40.3   | 29.5 | 27.9 |\n\n| DINOv2 ViT-B/16 on MS COCO | FG-ARI | MBO  |\n|----------------|--------|------|\n| AdaSlot (train step: 500000)| 39.0   | 27.36 |\n| BO-QSA (train step: 40000)| 37.64   | 27.07 |\n| MetaSlot (train step: 40000)| 40.13   | 27.45 |\n\n>While I agree that datasets such as CLEVR and ShapeStacks can be left out because of their simplicity, I think the MOVI datasets should be part of the evaluation.\n\nWe have added experimental results where MetaSlot serves as the grouper in the SlotContrast [1] evaluation framework, evaluated on the MOVi-C dataset. In these experiments, MetaSlot does not utilize SlotContrast losses or any temporal-specific enhancements. Due to limitations in time and computational resources, we used a batch size of 8 (compared to 64 in SlotContrast), 24 frames per sample, and trained for a maximum of 20,000 steps (compared to 100,000 in SlotContrast). MetaSlot demonstrates significant performance advantages. We will include the full comparison experiments in the appendix of the revised version. The parameters will be aligned with those used in SlotContrast to ensure the experiments can be thoroughly validated by the community. We will incorporate the above discussion into the revised version of the paper.\n\n| MOVi-C         | FG-ARI | MBO  |\n|----------------|--------|------|\n| SlotContrast | 62.4 | 30.6   |\n| MetaSlot       | 63.9   | 35.0 |\n\n\n>Only DINOv2 ViT-S is used as the backbone, leaving out larger backbones and alternative models such as CLIP or MAE.\n\nDue to limited computational resources and time constraints, we were unable to include CLIP-based experiments — we apologize for this. As demonstrated in the DINOv2 paper, MAE performs worse than DINOv2 and was therefore not considered. As noted earlier, we conducted experiments using MetaSlot with DINOv2 ViT-B/16 as the encoder and compared it against AdaSlot. Notably, DINOv2 ViT-B/16 has 85.8M parameters and is approximately four times larger than ViT-S/14 in network scale. We also included additional results using ViT-S/16 to further demonstrate the generalizability of MetaSlot. Additional results with more encoders will be included in a future revision of the paper.\n\n| DINOv2 ViT-B/16 on MS COCO | FG-ARI | MBO  |\n|----------------|--------|------|\n| AdaSlot (train step: 500000)| 39.00   | 27.36 |\n| BO-QSA (train step: 40000)| 37.64   | 27.07 |\n| MetaSlot (train step: 40000)| 40.13   | 27.45 |\n\n| DINOv2 ViT-S/16 on VOC   | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| BO-QSA | 20.2 | 34.5   | 39.0 | 38.0 |\n| MetaSlot | 21.7 | 36.1   | 40.2 | 39.1 |\n\n>An insightful research question could be: \"for years, we have used 7 slots for COCO, 11 for CLEVR, and 6 for VOC, but how optimal is this choice?\".\n\nThank you for your suggestion. We will further explore this issue in future work. Since the codebook and the aggregator module are jointly optimized, the performance of the aggregator—such as using a slot number that significantly exceeds the maximum number of objects in a scene—can affect the convergence of the codebook, especially during the early stages of training. To address this, we have conducted an additional analysis on MetaSlot's sensitivity to different slot numbers. The results show that the optimal slot number aligns well with the long-standing empirical choices commonly adopted in the object-centric learning community.\n\n| DINOSAUR on MS COCO   | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| MetaSlot, slot_num=5 | 25.5 | 37.8   | 29.3 | 27.4 |\n| MetaSlot, slot_num=7 | 22.4 | 40.3   | 29.5 | 27.9 |\n| MetaSlot, slot_num=11| 19.8 | 38.7   | 28.4 | 26.9 |\n| MetaSlot, slot_num=15| 15.9 | 36.0   | 27.2 | 26.0 |\n\n>In figure 2, the predicted masks tend to be bigger than the enclosed objects, for example around the people and the cows.\n\nWe can observe that when using MetaSlot for segmentation, the upper back of the cow exhibits tighter boundaries compared to those produced by BO-QSA in figure 2. Since DINOSAUR performs reconstruction on DINO's pre-trained feature maps, the attention maps are upsampled back to the original pixel space, and the slightly oversized boundaries relative to the true pixel edges are a result of this training approach [1].\n\nIn unsupervised object discovery tasks, object categories are not considered [1]. The high scores are maintained because the evaluation metrics for object discovery ignore class labels and instead use the best-matched IoU (40.3% in MetaSlot), making them more tolerant of mismatches compared to COCO mAP (64.6% in YOLOv4).\n\n>Can we simply see object-centric methods as a form of clustering? And can this perspective help us simplify existing methods rather than adding complexity?\n\nWe appreciate the reviewer’s insightful observation that Object-Centric Learning (OCL) can be interpreted as a clustering process. It leverages the Slot Attention mechanism to assign image feature maps to a limited number of slots via competitive binding—these slots effectively serve as cluster centroids, akin to those in Gaussian Mixture Models. However, achieving truly object-level clustering remains a core challenge in the field, as opposed to mere aggregation of low-level features into attribute-level or category-level representations. Slot Attention introduces an inductive bias by constraining the slot dimensionality to be significantly lower than that of the original features, thereby encouraging object-level abstraction.\n\nNonetheless, the standard formulation—using a fixed number of slots initialized from an identical isotropic Gaussian distribution—introduces two fundamental limitations:\n\n1. **Lack of semantic prior**: Since all slots are initialized from the same isotropic Gaussian, they lack sufficient inter-cluster separation at the early stage of training. This often leads to mixed assignment where features of different objects are grouped into the same slot, or a single object is incorrectly split across multiple slots, resulting in over-/under-segmentation.\n\n2. **Cluster-number misspecification**: A fixed slot number $K$ fails to match the varying number of true objects $N$ in dynamic scenes, leading to a model-order mismatch. When $K < N$, under-clustering (under-segmentation) occurs; when $K > N$, over-clustering (over-segmentation) arises, both of which impair object consistency.\n\nBO-QSA addresses the first issue by introducing learnable, slot-specific Gaussian priors—effectively planting $K$ more dispersed centroids during initialization, which alleviates semantic entanglement and helps stabilize background slots. However, the diversity of these priors remains limited by the fixed number of slots, which still constrains their effectiveness in real-world scenarios with high object diversity and variable object counts (e.g., COCO, VOC).\n\nMetaSlot overcomes both longstanding challenges in OCL by introducing a two-stage aggregation mechanism guided by prototype slots. Rich semantic priors are injected early on via these prototype slots, providing high-resolution object-level cues and enabling the initial centroids to exhibit clearer inter-class separation. At the same time, the prototype matching mechanism addresses cluster-number misspecification by allowing flexible alignment between dynamic object counts and slot representation.\n\nFurthermore, we inherit the bi-level optimization strategy from BO-QSA: the two-stage Slot Attention executes its full iterative process, but gradients are computed and backpropagated only at the final update step of the second-stage MSA. As a result, parameter updates rely solely on stabilized slot assignments, thereby reducing gradient noise induced by cluster-number mismatch and improving convergence efficiency.\n\n> Minor points\n\nFinally, thank you for your valuable suggestions regarding the details of our paper. Additionally, in MetaSlot, the Slot Attention (SA) used in the first stage and the Masked Slot Attention (MSA) used in the second stage share the same parameters. Therefore, MSA serves as the key component in enabling support for a dynamic number of slots.\n\n[1] Seitzer, Maximilian, et al. Bridging the Gap to Real-World Object-Centric Learning, ICLR 2023"}, {"review_id": "yge9gVAhnx", "reviewer": "Reviewer_gev3", "summary": "This paper proposes MetaSlot, a variant of Slot Attention bottleneck that utilizes vector quantization to identify the prototypes of slots and eliminate duplicate slots, thereby overcoming the limitation of the fixed number of slots. The authors further propose the second-stage aggregation based on quantized slots and other techniques, like noise annealing, to further improve object discovery results.", "questions": "Same as the weaknesses part mentioned above.\n\nI am glad to accept the paper if the author could address my concerns about weakness 2 and weakness 3.\n\nMoreover, there is an additional question: \n\n1. A potential concern arises from the slot deduplication procedure: when multiple objects with similar prototypes appear in an image, how does the method avoid erroneously removing semantically similar objects? For example, the third picture in Figure 2 with two persons in the foreground. The authors are encouraged to visualize the corresponding codebook ID of the two persons and the other slots in the codebook.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n1. The paper is clearly written and easy to follow. The author proposes a novel two-stage slot variant: the method first quantizes the slots and then refines these quantized slots with noise, resulting in the final aggregated slots. This approach offers a new and reasonable direction for slot quantization research. \n2. Notably, the quantized slots can be deduplicated, enabling a dynamic number of slots, as illustrated in Fig. 2. Furthermore, the method outperforms prior work like AdaSlot by achieving improved grouping results on real-world datasets. \n3. The author also provides extensive experimental results, evaluating the approach with three different types of decoders.\n\n**Weaknesses:**\n1. The authors are encouraged to provide object discovery visualizations on ClevrTex to further illustrate their method's effectiveness in the dynamic slot allocation. \n2. Additionally, the comparison in Figure 3 does not sufficiently support the claims of interpretability analysis. Specifically, the slot indices in BO-QSA do not necessarily correspond to clear visual concepts. To address this, the authors should consider following the approach used in SLATE by constructing a visual concept library with K-means clustering on the computed slots after $T$ iterations.\n3. The related work section also overlooks a substantial body of literature on slot-related vector quantization (VQ), such as SysBinder. Since incorporating the quantization module could naturally enhance the slot attention mechanism, it would be valuable for the authors to include comparisons with methods like SysBinder[1], COSA[2] and NLoTM [3]. \n\n[1] Singh, Gautam, Yeongbin Kim, and Sungjin Ahn. \"Neural systematic binder.\" ICLR 2023\n\n[2] Kori, Avinash, et al. \"Grounded Object-Centric Learning.\"   ICLR 2024\n\n[3] Wu, Yi-Fu, Minseung Lee, and Sungjin Ahn. \"Neural language of thought models.\"  ICLR 2024.\n\n[4] Wu, Yi-Fu, Minseung Lee, and Sungjin Ahn. \"Object-Centric Semantic Vector Quantization.\"  NeurIPS 2023 Causal Representation Learning Workshop.\n\n[5] Kirilenko, Daniil, et al. \"Quantized disentangled representations for object-centric visual tasks.\" PReMI 2023.", "was_revised": true, "final_justification": "The discussion with the authors satisfactorily addressed my main concern regarding the lack of comparisons with other slot quantization methods. Given this, I have increased my final score. No other major issues remain.", "rebuttal": "We appreciate the reviewer’s constructive feedback and address the concerns as follows.\n\n>  Provide object discovery visualizations on ClevrTex\n\nAs visualizations cannot be included during the rebuttal stage, we will add a dedicated section in the appendix of the revised paper to present Figure 7 (visualization results on the ClevrTex dataset) and provide a detailed discussion of this phenomenon.\n\nIn ClevrTex’s simple and consistent scenes, we observe that merely adding noise annealing to Slot Attention enables the model to bind each object to a distinct slot while automatically leaving redundant slots empty. Specifically, without noise annealing, the model often splits the background across multiple slots, whereas with noise annealing, the background consistently remains confined to a single slot.\n\nSimilarly, in the Clevr dataset, shadows caused by lighting often lead the original Slot Attention to mistakenly assign parts of the background to object slots. In contrast, noise annealing helps maintain clean and exclusive slots for objects.\n\n> The slot indices in BO-QSA do not necessarily correspond to clear visual concepts. To address this, the authors should consider following the approach used in SLATE by constructing a visual concept library with K-means clustering on the computed slots after $T$ iterations.\n\nIn this section, we would like to clarify that the ability of slot initialization to endow slots with concept-binding capabilities is not the central focus of this paper. Rather, this argument originates from the appendix of the BO-QSA paper, upon which MetaSlot builds and extends. In the BO-QSA appendix, visualizations on single-category datasets such as CUB-200 Birds and Stanford Dogs are presented to illustrate concept binding without the need for clustering. These visualizations clearly show that distinct visual concepts (e.g., bird body, background, tree trunk, leaves) are consistently assigned to separate slots, and that the slot binding order remains stable across the dataset. Notably, in datasets with prominent background elements, a dedicated slot consistently captures the background throughout the entire dataset.\n\nHowever, in more complex and diverse real-world datasets such as COCO and VOC, where a wide range of object categories exist, the visual representations do not easily reveal clear semantic groupings. To address this, we employed t-SNE clustering to visualize the distribution of BO-QSA and MetaSlot’s prior slots within the posterior slots. The results show that the priors provide good coverage of the posterior distribution and are highly similar to the resulting object-level posterior slots.\n\nAs we are unable to present images during the rebuttal phase, we kindly refer the reviewer to Figures 10, 11, and 12 in the appendix of the BO-QSA paper. Additionally, in the revised version, we will include visual comparisons of ObjectRoom generated using both BO-QSA and the original Slot Attention, as well as the corresponding t-SNE clustering visualizations, in the appendix.\n\nMoreover, you have accurately captured the essence of our object concept library construction. While SLATE applies k-means clustering as a post-processing step—grouping slots from the entire dataset into a more compact prototype set after training—MetaSlot integrates this process directly into training via a vector quantization (VQ) codebook, effectively performing end-to-end k-means clustering over the dataset-wide slot representations. We employ BO-QSA in the first stage to initialize clustering and fully leverage the rich semantics of the prototype library to guide the second-stage aggregation, thereby achieving superior performance. Through careful design, MetaSlot enables end-to-end joint optimization, surpassing post-hoc approaches like SLATE in both elegance and effectiveness.\n\n>It would be valuable for the authors to include comparisons with methods like SysBinder, COSA and NLoTM\n\nThank you for pointing out these three valuable and interesting works. First, the vector quantization mechanisms used in SysBinder and NLoTM operate at the attribute level within each individual slot. However, such mechanisms do not necessarily guarantee improved representation performance in unsupervised learning. This is because, in synthetic datasets, object attributes are relatively fixed—such as size, color, material, and shape—and all objects in the scene can typically be described using fewer than ten shared attribute labels. In contrast, real-world datasets like COCO and VOC contain objects with infinitely divisible and non-shared attribute information. For example, the object \"person\" cannot be meaningfully described using the same set of attribute labels as the object \"mouse.\" We believe this is also a primary reason why these methods were not evaluated on real-world datasets. \n\nFurthermore, the initial intuition behind MetaSlot is inspired by Platonic philosophy. In Plato’s theory of Forms, sensible particulars are intelligible only insofar as they “participate” (*metechē*) in a transcendent Form (*Phaedo* 100c–d). Similarly, Aristotle maintains in the *Categories* that accidents belong to substances only as predicates of discourse, not as essential ingredients shared by all beings (*Categories* 2a11–19). \n\nAt the practical level, SysBinder uses slots as queries that interact with a prototype memory via cross-attention, enabling soft access to the memory bank. This mechanism differs from standard vector quantization, which typically relies on nearest-neighbor search to select discrete representations. We further provide evaluation results of SysBinder and NLoTM on both the COCO and ClevrTex datasets, showing that MetaSlot significantly outperforms both methods. Although SysBinder show a slight advantage in FG-ARI on ClevrTex, they perform worse than the DINOSAUR baseline on other key metrics such as ARI, MBO, and mIoU. Additionally, the SysBinder paper only reports FG-ARI and does not include ARI, MBO, or mIoU—metrics widely regarded as standard for evaluating object discovery.\n\nThe poor performance of SysBinder and NLoTM on COCO further supports the above analysis. Moreover, NLoTM exhibited mode collapse on the COCO dataset. In contrast, MetaSlot outperforms the DINOSAUR baseline across all four evaluation metrics.\n\nAs for CoSA, the number of objects is indirectly obtained via spectral decomposition, rather than learned adaptively across the dataset by identifying object prototypes. Moreover, CoSA derives its codebook from prior slots. While this prior can effectively guide the refinement process to form the posterior slot distribution, there is no explicit alignment mechanism between the prior and the posterior. In contrast, MetaSlot updates the prototype slots using the posterior slots produced by MSA, ensuring that the distribution of prototypes remains precisely aligned with that of the posterior slots.\n\nSince CoSA did not release its implementation code and only reported ARI as the evaluation metric—which, compared to FG-ARI and MBO, is less indicative of object discovery performance—we did not include a direct comparison with this method. Finally, we will incorporate a concise version of the above discussion into the Related Work section and add the corresponding experiments to the Experiments section.\n\n| MS COCO   | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| SysBinder |  16.8 | 37.7   | 27.2 | 26.0 |\n| NLoTM | 57.8 | 14.7   | 23.6 | 17.8 |\n| DINOSAUR | 18.2 | 35.0   | 28.3 | 26.9 |\n| MetaSlot | 22.4 | 40.3   | 29.5 | 27.9 |\n\n| CLEVRTEX   | ARI  | FG-ARI | MBO  | mIoU |\n|----------------|------|--------|------|------|\n| SysBinder | 21.7 | 91.4   | 46.8 | 46.5 |\n| NLoTM | 21.5 | 88.7   | 44.1 | 43.1 |\n| DINOSAUR | 50.7 | 89.4   | 53.3 | 52.8 |\n| MetaSlot | 64.6 | 89.6   | 55.2 | 54.5 |\n\n>Visualize the corresponding codebook ID of the two persons and the other slots in the codebook.\n\nThank you for raising this question—it allows us to further clarify the internal mechanism of MetaSlot. As the current model does not explicitly disentangle spatial information from object representations, we have also acknowledged this limitation in the appendix.\n\nIn MetaSlot, **each prototype slot encodes not only an “object concept” but also its associated spatial attributes.** For example, Figure 3 shows that #slot 445 represents the concept of a “person” and is bound to the right side of the image, while #slot 337 also corresponds to “person” but is located at the center of the scene. Similarly, in the third image of Figure 2, the person on the left is mapped to #slot 133 (“person” + scene left), while the person on the right corresponds to #slot 187 (“person” + right side with greater foreground prominence). These examples indicate that the prototype slots naturally encode both semantic and spatial information, rather than representing abstract, spatially disentangled concept vectors.\n\nDue to the constraints of the rebuttal phase, we are unable to include all visualizations here. However, we will provide full examples illustrating the correspondence between prior and posterior prototype slots in our released code to facilitate deeper exploration by the community.\n\nAt the same time, this observation highlights an open question we discuss in the Limitations section: **how can we disentangle object identity from spatial information while maintaining dynamic slot allocation and preventing over-segmentation via codebook matching?**\n\nOverall, the key contribution of this work lies in introducing the codebook mechanism to support a variable number of slots and in demonstrating the critical role of semantically rich initialization priors for achieving high-quality object discovery with Slot Attention."}]

Author rebuttal:

Meta-review: This paper studies the problem of determining the number of components (or slots) in object-centric learning. The method, which is based on a VQ codebook of object prototypes, can be applied to several existing frameworks to yield improvements as is shown. Improvements are shown on real-world datasets, thereby contributing to the state-of-the-art in this area. 

Reviewers were initially largely positive about the contribution, though there were some issues raised, such as about comparisons to other methods for object-centric learning, or further ablations and analysis, which the authors addressed during the rebuttal. One reviewer lists limited novelty and lack of theoretical analysis as a remaining concern that wasn't fully addressed, even after the rebuttal. However, no major issues remain, and all reviewers are in favor of acceptance.

In summary, the AC agrees with the reviewers that this is a valuable contribution that will certainly be of interest to object-centric learning, and that (after rebuttal) the quality is sufficient to warrant acceptance.
Decision: Accept (poster)