Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment
Authors: Bac Nguyen
Yuhta Takida
Naoki Murata
Chieh-Hsin Lai
Toshimitsu Uesaka
Stefano Ermon
Yuki Mitsufuji
Venue: ICLR 2026 Poster (2026)
Keywords: Object-centric learning
diffusion models
contrastive learning
slot attention
compositionality
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Slot Attention (SA) with pretrained diffusion models has recently shown promise for object-centric learning (OCL), but suffers from slot entanglement and weak alignment between object slots and image content. We propose Contrastive Object-centric Diffusion Alignment (CODA), a simple extension that (i) employs register slots to absorb residual attention and reduce interference between object slots, and (ii) applies a contrastive alignment loss to explicitly encourage slot–image correspondence. The resulting training objective serves as a tractable surrogate for maximizing mutual information (MI) between slots and inputs, strengthening slot representation quality. On both synthetic (MOVi-C/E) and real-world datasets (VOC, COCO), CODA improves object discovery (e.g., +6.1% FG-ARI on COCO), property prediction, and compositional image generation over strong baselines. Register slots add negligible overhead, keeping CODA efficient and scalable. These results indicate potential applications of CODA as an effective framework for robust OCL in complex, real-world scenes. Code and pretrained models are available at https://github.com/sony/coda.

Published as a conference paper at ICLR 2026
IMPROVED OBJECT-CENTRE DIFFUSION LEARNING WITH REGISTERS AND CONTRASTIVE ALIGNMENT
Bac Nguyen'r, Yuhta Takida', Naoki Murata', Chieh-Hsin Lai', Toshimitsu Uesaka', Stefano Ermon", Yuki Mitsufuji *Sony AI, Stanford University, SSony Group Corporation
ABSTRACT
Slot Attention (SA) with pretrained diffusion models has recently shown promise for object-centric learning (OCL), but suffers from slot entanglement and weak alignment between object slots and image content. We propose Contrastive Object- centric Diffusion Alignment (CODA), simple extension that (i) employs register slots to absorb residual attention and reduce interference between object slots, and (ii) applies contrastive alignment loss explicitly encourage slot-image correspondence. The resulting training objective serves as a tractable surrogate for maximizing mutual information (MJ) between slots and inputs, strengthening slot representation quality. On both synthetic (MOVi-C/E) and real-world datasets (VOC, COCO), CODA improves object discovery (e.g., +6. %% FG-ARI on COCO), property prediction, and compositional image generation over strong baselines. Register slots add negligihle overhead, keeping CCDA efficient and scalable. These results indicate potential applications of CODA as effective framework for robust OCL in complex, real-world scenes. Code and pretrained models are available at https: / / thub. com/ sony/ coda.
INTRODUCTION
Object-centric learning (OCL) aims to decompose complex scenes into structured, interpretable object representations, enabling downstream tasks such as visual reasoning (Assouel et al., 2022; D A'mario et al., 2021), causal inference (Schölkopf et al., 2021; Zholus et al., 2022), world modeling (Ke et al., 2021), robotic control (Haramati et al., 2024), and compositional generation (Singh et al., 2022a). Yet, learning such compositional representations directly from images remains a core challenge. Unlike text, where words naturally form composable units, images lack explicit boundaries for objects and concepts. For example, in a street scene with pedestrians, cars, and traffic lights, a model must disentangle these entities without labels and also capture their spatial relations (e.g.. a person crossing in front of a car). Multi-object scenes add further complexity: models must not only detect individual objects but also capture their interactions. As datasets grow more cluttered and textured, this becomes even harder. Manual annotation of object boundaries or compositional structures is costly, motivating the need for fully unsupervised approaches such as Slot Attention (SA) (Locatello et al., 2020). While effective in simple synthetic settings, SA struggles with large variations in real-world images, limiting its applicability to visual tasks such as image or video editing.
Combining SA with diffusion models has recently pushed forward progress in OCL (Jiang et al., 2023; Wu et al., 2023; Akan & Yemez, 2025). In particular, Stable-LSD (Jiang et al., 2023) and SlotAdapt (Akan & Yemez, 2025) achieve strong object discovery and high-quality generation by leveraging pretrained diffusion backbones such as Stable Diffusion (Rombach et al., 2022) (SD). Nevertheless, these approaches still face key challenges. First, illustrated in Fig. (left), they often suffer from slot entanglement, where slot encodes features from multiple objects or fragments of them, leading unfaithful single-slot generations. This entanglement degrades segmentation quality and prevents composable generation to novel scenes and object configurations. Second, they exhibit weak alignment, where slots fail to consistently correspond to distinct image regions, especially on real-world images. As shown in our experiments, slots often suffer from over-segmentation (splitting one object into multiple slots), under-segmentation (merging multiple
"Correspondence bac. www.gnconggssnyy..... com

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Figure 1:
Image generation from individual slots. Top: slot masks. Bottom: generated images. Both methods can reconstruct the full scene when conditioned on all slots (last column). However, Stable-LSD (without register slots) fails to generate images from individual slots. Our method yields faithful single-concept generations, demonstrating disentangled and well-aligned slots.
objects into one slot), or inaccurate object boundaries. Together, these two issues reduce both the accuracy of object-centric representations and their utility for compositional scene generation.
In response, we propose Contrastive Object-centric Diffusion Aligmment (CODA), a slot-attention model that uses pretrained diffusion decoder to reconstruct the input image. CODA augments the model with register slots, which absorb residual attention and reduce interference between object slots, and contrastive objective, which explicitly encourages slot-image alignment. As illustrated in Fig. 1 (right), CODA faithfully generates images from both individual slots as well as their compositions. In summary, the contributions of this paper can be outlined as follows.
(i) Registeraaugmented slot diffusion. We employ register slots that are independent of the input image into slot diffusion. Although these register slots carry no semantic information, they act as attention sinks, absorbing residual attention mass so that semantic slots remain focused on meaningful object-concept associations. This reduces interference between object slots and mitigating slot entanglement (Section 4.1).
(ii) Mitigating conticondiiin bias. To reduce the influence text-conditioning biases inherited from pretrained diffusion models, we finetune the key, value, and output projections in cross- attention layers. This adaptation further improves alignment between slots and visual content, ensuring more faithful object-centric decomposition (Section 4.2).
(iii) Contrastive alignment objective. We propose a contrastive loss that ensures slots capture concepts present in the image (Section 4.3). Together with the denoising loss, our training objective can be viewed as a tractable surrogate for maximizing the mutual information (MI) between inputs and slots, improving slot representation quality (Section 4.4).
(iv) Comprehensive evaluation. We demonstrate that CODA outperforms existing unsupervised Liffusion-based approaches across synthetic and real-world benchmarks in object discovery (Section 5.1), property prediction (Section 5.2), and compositional generation (Section 5.3). On the VOC dataset, CODA improves instance-level object discovery by +3.88% mBO' and +3.97% mIoU', and semantic-level object discovery by +5.72% mBO and +7.00% mloUs, On the COCO dataset, improves the foreground Adjusted Rand Index (FG-ARI) by +6.1+....99 14%.
RELATED WORK
Object-centric learning (OCL). The goal of OCL is to discover compositional object representations from images, enabling systematic generalization and stronger visual reasoning (D' Amario et al., 2021; Assouel al., 2022). Learning directly from raw pixels is difficult, so previous works leveraged weak supervision (e.g., optical flow (Kipf et al., 2022), depth (Elsayed et al., 2022), text (Xu et al., 2022), pretrained features (Seitzer et al., 2023)), or auxiliary losses that guide slot masks toward moving objects (Bao et al., 2022; 2023; Zadaianchuk et al., 2023). Scaling OCL to complex datasets has been another focus: DINOSAUR (Seitzer et al., 2023) reconstructed self-supervised features to segment real-world images, and FT-DINOSAUR (Didolkar et al., 2025) extended this via encoder finetuning for strong zero-shot transfer. SLATE (Singh et al., 2022a) and STEVE (Singh et al., 2022b) combined discrete VAE tokenization with slot-conditioned autoregressive transformers, while SPOT (Kakogeorgiou et al., 2024) improved autoregressive decoders using patch permutation and attention-basee self-training. Our work builds on SA, but does not require any additional supervision.
Diffusson models for OCL. Recent works explored diffusion models (Sohl-Dicksted et al., 2015; Rombach et al., 2022) as slot decoders in OCI. Different methods vary in how diffusion models
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integrated. For example, SlotDiffusion (Wu et al., 2023) trained a diffusion model from scratch, while Stable-LSD (Jiang et al., 2023). GLASS (Singh et al., 2025), and SlotAdapt (Akan & Yemez, 2025) leveraged pretrained diffusion models. Although pretrained models offer strong generative capabilities, they are often biased toward text-conditioning. To address this issue, GLASS (Singh et al., 2025) employed cross-attention masks as pseudo-ground truth to guide SA training. Unlike GLASS, CODA does not rely on supervised signals such as generated captions. SlotAdapt (Akan & Yemez, 2025) introduced adapter layers to enable new conditional signals while keeping the base diffusion model frozen. In contrast, CCDA simply finetunes key, value, and output projections in cross- attention, without introducing additional layers. This ensures full compatibility with off-the-sheli diffusion models while remaining conceptually simple and computationally efficient.
Contrastive learning for OCL. Training SA with only reconstruction losses often leads to unstable or inconsistent results (Kim et al., 2023). To improve robustness, several works introduced contrastive objectives. For example, SlotCon (Wen al., 2022) applied the InfoNCE loss (Oord et al., 2018) across augmented views of the input image to enforce slot consistency. Manasyan et al. (2025) used contrastive loss to enforce the temporal consistency for video object-centric models. In contrast, CODA tackles compositionality by aligning images with their slot representations, enabling faithful generation from both individual slots and their combinations. Unlike Jung et al. (2024), who explicitly maximize likelihood under random slot mixtures and thus directly tune for compositional generation, CODA focuses on enforcing slot-image alignment; its gains in compositionality arise indirectly from improved disentanglement. Although CODA uses negative loss term, similar to negative guidance in diffusion models (Karras et al., 2024), the roles are fundamentally different. Karras et al. (2024) apply negative guidance during sampling to steer the denoising trajectory, whereas CODA uses contrastive loss during training to improve dlot-image alignment.
3 BACKGROUND
Slot Attention (Locatello et al., 2020) (SA). Given input features E Mropor of an image, the goal of OCL is to extract sequence E Nx of N slots, where each slot is a Delot- dimensional vector representing a composable concept. In SA, we start with randomly initialized slots as s00) N Once initialized, SA employs an iterative mechanism to refine the slots. In particular, slots serve queries, while the input features serve as keys and values. Let q, k, and denote the respective linear projections used in the attention computation. Given the current slots (!) and input features f, the update rule can be formally described as
(+++1 GRU(s (t), u (1)
where
n
Here, attention readouts are aggregated and refined through a Gated Recurrent Unit (Cho et al., 2014) (GRU). Unlike self-attention (Vaswani et al., 2017), the softmax function in SA is applied along the slot axis, enforcing competition among slots. This iterative process is repeated for several steps, and the slots from the final iteration are taken the slot representations. Finally, these slots are passed to a decoder trained to reconstruct the input image. The slot decoder can take various forms, such as an MLP (Watters et al., 2019) or autoregressive Transformer (Vaswani et al., 2017). Interestingly, recent works (Jiang et al., 2023; Singh et al., 2025; Akan & Yemez, 2025) have shown that using (latent) diffusion models as slot decoders proves to be particularly powerful and effective in OCL.
Latent difflusonn models (Rombach et al., 2022) (LDMs). Diffusion models are probabilistic models that sample data by gradually denoising Gaussian noise (Sohi-Dickstein et al., 2015; Song et al., 2021; Ho et al., 2020). The forward process progressively corrupts data with Gaussian noise, while the reverse process learns to denoise and recover the original signal. To improve efficiency, SD performs this process in a compressed latent space rather than pixel space. Concretely, a pretrained autoencoder maps an image to a latent vector z E Z, where U-Net denoiser iteratively refines noisy latents. Consider variance preserving process that mixes the signal z with Gaussian noise ~ ~N,( I), given by z, VØ(()zz + vo(->Je, where (.) is the sigmoid function and 7y is the log hignal-to-novl ratio. Let Eg EZ 7, c) denote a denoiser parameterized by 0 that predicts the Gaussian noise from noisy latents Z,, conditioned on an external signal In SD, conditioning is implemented through cross- atteentttn,,on, which computes attention between the conditioning signal and the features produced by U-Net. Training diffusion models is formulated as a noise prediction
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Input
Slot Attention
DINOv2
distunce
K
ca(#, 7,8)
Byy
U-Net
moximize
distance
1 fronen lcanallee
rogister skot
cg(%,, 7,
Figure 2: Overview of CODA. The input image is encoded with DINOv2 and processed by Slot Attention (SA) to produce slot representations. The semantic slots s, together with register slots serve as conditioning inputs for the cross-attention layers of pretrained diffusion model. SA is trained jointly with the key, value, and output projections of the cross-attention layers using a denoising objective that minimizes the mean squared error between the true and predicted noise. In addition, a contrastive loss is applied to align each image with its corresponding slot representations.
problem, where the model learns to approximate the true noise e added during the forward process, co (2,, 7, [
min 6
Biz.c). [||e
with (z, c) sampled from data distribution p(z, c). Once training is complete, sampling begins from random Gaussian noise, which is iteratively refined using the trained denoiser.
PROPOSED METHOD
As summarized in Fig. 2, CODA builds on diffusion-based OCL by extracting slot sequences from DI- NOv2 features (Oquab et al., 2024) with SA and decoding them using pretrained SD v1.5 (Rombach et al., 2022). To address the challenges of slot entanglement and weak alignment, CODA introduces three components: (i) register slots to absorb residual attention and keep object slots disentangled, (ii) finetuning of cross-attention keys and queries to mitigate text-conditioning bias, and (iii) a contrastive alignment loss to explicitly align images with their slots. These modifications yield disentangled, well-aligned slots that enable faithful single-slot generation and compositional editing.
4.1 REGISTER SLOTS
An ideal OCL model should generate semantically faithful images when conditioned on arbitrary subsets of slots. In practice, however, most diffusion-based OCL methods fall short of this goal. As discussed the introduction, decoding single slot typically yields distorted or semantically uninformative outputs. Although reconstructions from the full set of slots resemble the imput images, this reliance reveals a strong interdependence among slots (see Section D for more detailed analysis). Such slot entanglement poses challenge for compositionality, particularly when attempting reuse individual concepts novel configurations.
To address this problem, add input- spputinndependent register slots that act residual attention sinks, absorbing shared or background information and preventing object slots from mixing. Intuitively, register slots semantically empty but structurally valid inputs, making them natural placeholders for slots that can capture residual information without competing with object representations. We obtain these register slots by passing only padding tokens through the SD text encoder, a pretrained ViT-I /14 CLIP (Radford et al., 2021). Formally, let pad denote the padding token used to ensure fixed-length prompts in text-to-image SD. By encoding the sequence [pad,... ..., .aad] with the frozen text encoder', we obtain a fixed-length sequence of frozen embeddings serving as register slots r.
For SD v1.5, padding tokens are used, resulting in register slots.
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We also explore alternative design with trainable register slots in Section E.2, and find that while learnable registers can also mitigate slot entanglement, our simple fixed registers perform best.
Why do register slots mitigate slot entanglement? The softmax operation in cross-attention forces attention weights to sum to one across all slots. When a query from U-Net features does not strongly match any semantic slot, this constraint causes the attention mass to spread arbitrarily, weakening slot-concept associations. Register slots serve as placeholders that absorb this residual attention, giving the model extra capacity to store auxiliary information without interfering with semantic slots. This leads to cleaner and more coherent slot-to-coneept associations. Consistent with this view, we observe in Section that a substantial fraction of attention mass is allocated register slots. A similar phenomenon has been reported in language models. where sof tmax normalization causes certain initial tokens to act as attention sinks (Xiao et al., 2024; Gu al., 2025), absorbing unnecessary attention mass and preventing it from distorting meaningful associations.
In a related approach, Akan Yemez (2025) introduced an additional embedding by pooling from either generated slots or image features. Unlike our method, their embedding is injected directly into the cross-attention layers and is explicitly designed to capture global scene information. While this might provide contextual guidance, it ties the model to input-specific features, reducing flexibility in reusing slots across arbitrary compositions. In contrast, our register slots are independent of the input image, making them better suited for compositional generation.
4.2 FINETUNING PROSS-ATTENTION
SD is trained large-scale image-text pairs, directly using its pretrained model slot decoder introduces a text-conditoonin bias: the model expects text embeddings and tends to prioritize language-driven semantics over slot-level representations (Akan & Yemez, 2025). This mismatch weakens the fidelity of slot-based generation. Prior works have approached this issue in different ways. For example, Wu et al. (2023) trained diffusion models from scratch, thereby removing text bias but sacrificing generative quality due to limited training data. More recently, Akan & Yemez (2025) proposed adapter layers (Mou et al., 2024) to align slot representations with pretrained diffusion models, retaining generation quality but still relying on text-conditioning features.
In contrast, we adopt a lightweight adaptation strategy: finetuning only the key, value, and output projections in cross-attention layers (Kumari et al., 2023). This allows the model to better align slots with visual content, mitigating rext-conditioning bias while preserving the expressive power of the pretrained diffusion backbone. We find this minimal modification sufficient to eliminate the bias introduced by text conditioning. Unlike the previous approaches, our method is both computationally and memory efficient, requiring no additional layers or architectural modifications. This makes approach not only effective but also conceptually simple. Formally, let & denote the parameters of SA, the denoising objective for diffusion models can be written as
Lam Lin(ch) (s) 0) = E.,,,)),.y [||e -e (2
7, 1
where (z are sampled from p(z)p z). In practice, s is not computed directly from z, but rather from DINOv2 features of the image corresponding to The U-Net conditioned on the concatenation (s, F) of semantic slots s and register slots F. During training, the parameters of SA are optimized jointly with the finetuned key, value, and output projections of SD, while other parameters are kept frozen.
4.3 CONTRASTIVE ALIGNMENT
The goal of OCI is to learn composable slots that capture distinct concepts from an image. However, in diffusion-based OCI. frameworks, slot conditioning only serves as auxiliary information for the explicit supervision ensure that slots capture concepts present in the image. As a result, slots may drift toward arbitrary or redundant representations, limiting their interpretability and compositionality.
denoising loss, providing
To address this, we propose a contrastive alignment objective that explicitly aligns slots with image content while discouraging overlap between different slots. Intuitively. the model should assign high likelihood to the correct slot representations and low likelihood to mismatched (negative) slots. Concretely, in addition to the standard denoising loss in Eq. (1), we introduce contrastive loss
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defined as the negative of denoising loss evaluated with negative slots s:
La(Ø) = -x(,,)), [|| Eg(2,, 7, [
where (2,8) are sampled from (5 z) and 0 denotes stop-gradient parameters of O. Minimiz- ing Eq. (1) increases likelihood under aligned slots, while minimizing Eq. (2) decreases likelihood under mismatched slots. We freeze the diffusion decoder and update only the SA module in Eq. (2), preventing the decoder from trivially reducing contrastive loss by altering its generation process. This ensures that improvements come from better slot representations rather than shorteut solutions. As confirmed by our ablations (see Table 5), unfreezing the decoder leads to unstable training and degraded performance across all metrics.
Finally, combining Eqs. (1) and (2), the overall training objective of CODA is defined as
L( 0) Lam 0) (
where Aet 2 0 controls the trade-off between the denoising and contrastive terms. We study the effect of varying Aet in Section E.4. This joint objective forms a contrastive learning scheme that acts as surrogate for maximizing the MI between slots and images, as further discussed in Section 4.4.
Strategy for composing negative slots. A straightforward approach for obtaining negative slots is to sample them from unrelated images. However, such negatives are often too trivial for the decoder, providing little useful gradient signal. To address this, we construct hard negatives- more informative mismatches that push the model to refine its representations more effectively (Robinson al., 2021). Concretely, given two slot sequences, and s', extracted from distinct images x and x', we form negatives for by randomly replacing subset of slots in s with slots from s'. This produces mixed slot sets that only partially match the original image, creating harder and more instructive negative examples. In our experiments, replace half of the slots in with those from s', and provide an ablation over different replacement ratios in Section E.5. A remaining challenge that naive mixing can yield invalid combinations, e.g-, omitting background slots combining objects with incompatible shapes or semantics. To mitigate this, we share the slot initialization between x and x'. Because initialization correlated with the objects each slot attends to, sampling from mutually exclusive slots under shared initialization is more likely to produce semantically valid negatives than purely random mixing (Jung et al., 2024).
4.4 CONNECTION WITH MUTUAL INFORMATION
A central goal of our framework is to maximize MI between slots and the input image, that slots capture representations that are both informative and compositional. To make this connection explicit, we reinterpret our training objective in Eq. (3) through the lens of MI. We begin by defining the optimal conditional denoiser, i.e., the minimum mean square error (MMSE) estimator of e from a noisy channel Zy, which mixes and noise level y, conditioned on slots
E(2,, 7,
E Ee arg min Ep(eip(ass) [||e ē(2..7..)
E(Z,, y, s)
By approximating the regression problem with a neural network, we obtain an estimate of the MMSE denoiser, which coincides with the denoising objective of diffusion model training. Let š denote negative slots sampled from a distribution ( z). Under this setup, we state the following theorem.
Theorem 1. Let z and be two random variables, and let denote sample from distribution (5 z). Consider the diffusion process 2., = Va(r)z + vo(-7)e, with (O, Let é(Z.,, s) denote the MMSE estimator of e given (z,, s). Then the negative of mutual information (MI) between z and s admits the following form:
1 L.
-I(z;s)
(Ei.s), [[lc- E(2,,
Y, s - Ez,,),. [[|c (llc-E(z,:1 E(2,,,, 7)811 dy
2 00
(4)
+ Eg DDL z)[pp($ 2)) DKL(( z)]l((s))]
Direct optimization of Eq. (4) is infeasible, both due to the high sample complexity and the difficulty of evaluating the KL-divergence terms. The quantity instead provides a practical handle: measures the denoising gap between aligned and mismatched slots, and thus serves tractable

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surrogate for MI. For this reason, we adopt the training objective in Eq. (3), which aligns directly with A. Since the register slots F are independent of the data, they do not influence [(z; s). Furthermore, when š are sampled independently of such that q(š z) p(š), the KL-divergence terms in Eq. (4)
can be reinterpreted as dependency measures between s and z:
Corollary 1. With the additional assumption g( 2) p($) in Theorem 1, it follows that
A - (2;;s) -
(S)
Minimizing A therefore corresponds to maximizing MI plus additional reverse KL-divergence in Eq. (5). Intuitively, this reverse KL-divergence contributes by rewarding configurations where the joint distribution p(z,s) and the product of marginals p(z) (()) disagree in the opposite direction of MI. In combination with the forward KL in MI, this enforces divergence in both directions, thereby promoting stronger statistical dependence between and Proofs are provided in Section A.
EXPERIMENTS
We design our experiments to address the following key questions: (i) How well does CODA perform on unsupervised object discovery across synthetic and real-world datasets? (Section 5.1) (ii) How effective are the learned slots for downstream tasks such as property prediction? (Section 5.2) (iii) Does CODA improve the visual generation quality of slot decoders? (Section 5.3) (iv) What the contribution of each component in CODA? (Section 5.4) To answer these questions, CODA is compared against state-of-the-an fully unsupervised OCI, methods, described in Section B.2.
Datasets. Our benchmark covers both synthetic and real-world settings. For synthetic experiments, we use two variants of the MOVi dataset (Greff et al., 2022): MOVi-C, which includes objects rendered over natural backgrounds, and MOVi-E, which includes more objects per scene, making it more challenging for OCL. For real-world experiments, we adopt PASCAL VOC 2012 (Everingham et al., 2010) and COCO 2017 (Lin et al., 2014), two standard benchmarks for object detection and segmentation. Both datasets substantially increase complexity compared to synthetic ones, due to their large number of foreground classes. VOC typically contains images with single dominant object, while COCO includes more cluttered scenes with two or more objects. Further dataset and implementation details are provided in Section B.
5.1
OBJECT DISCOVERY
Object discovery evaluates how well slots bind to objects by predicting a set of masks that segment distinct objects in an image. Following prior works, we report the FG-ARI, clustering similarity metric widely used in this setting. However, FG-ARI alone can be misleading, as it may favor either over-segmentation under-segmentation (Kakogeorgiou et al., 2024; Wu et al., 2023; Seitzer et al., 2023), thus failing to fully capture segmentation quality. To provide a more comprehensive evaluation, we also report mean Intersection over Union (mloU) and mean Best Overlap (mBO). Intuitively, FG- ARI reflects instance separation, while mBO measures alignment between predicted and ground-trutt masks. On real-world datasets such VOC and COCO, where semantic labels are available, compute both mBO and mloU at two levels: instance-level and llaes- Instance-level metrics assess whether objects of the same class are separated into distinct instances, whereas class-level metrics measure semantic grouping across categories. This dual evaluation reveals whether a model tends prefer instance-based or semantic-based segmentations.
Table 1 shows results on synthetic datasets. CODA outperforms on both MOVi-C and MOVi-E. On MOVi-C, it improves FG-ARI by +7.15% and mloU by +7. +7.75% over the strongest baseline. On MOVi- E. which contains visually complex scenes, it improves FG-ARI by +2.59% and mloU by +3.36%. In contrast, SLATE and L.SD struggle to produce accurate object segmentations. Table 2 presents results on real-world datasets. CODA surpasses the best baseline (SlotAdapt) by +6.14% in FG-ARI on COCO. CODA improves instance-level object discovery by +3.88% mBO' and +3.97% mloU', and semantic-level object discovery by +5.72% mBO and +7.00% mIoU on VOC. Qualitative results in Fig. 5 further illustrate the high-quality segmentation masks produced by CODA. Overall, these results demonstrate that CODA consistently outperforms diffusion- frafusdio--bsedd OCL baselines by significant margin. The improvements highlight its ability to obtain accurate segmentation, which facilitates compositional perception of complex scenes.
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Table Unsupervised object segmentation results on synthetic datasets, Results of other methods are reported from (Jiang et al., 2023; Akan & Yemez, 2025).
MOVi-C
SLATE
SLATE*
LSD
Ours
MOVi-E
SLATE
SLATE
LSD
SlotAdapt
Ours
mBO (1)
39.37
38.17
45.57
46.55
mBO (1)
30.17
22.17
38.96
43.38
43.35
mloU (1)
37.75
36.44
44.19
51.94
mloU (1)
28.59
20.63
37.64
41.85
45.21
FG-ARI (1)
49.54
52.04
51.98
59.19
FG-ARI (T)
46.06
45.25
52.17
56.45
59.04
Table 2:
Unsupervised object segmentation results on real-world datasets. i indicates results taken from (Wu ct al., 2023), while results for other methods are taken from their respective papers.
VOC
FG-ARI mBO't mBO't m mloU
COCO
FG-ARIf mBO't mBO I mloU?*
MLP decoders
MLP decoders
SA
12.3
24.6
24.9
SA
21.4
17.2
19.2
DINOSAUR
24.6
39.5
40.9
DINOSAUR
40.5
27.7
30.9
Auloregressive decoders
Autoregressive decoders
SLATE!
15.6
35.9
41.5
SLATE
32.5
29.1
33.6
DINOSAUR
24.8
44.0
51.2
DINOSAUR
34.1
31.6
39.7
SPOT w/o ENS
19.7
48.1
55.3
46.5
SPOT w/o ENS
37.8
34.7
44.3
32.7
SPOT w/ ENS
19.7
48.3
55.6
46.8
SPOT w/ ENS
37.8
35.0
44.7
33.0
Diffussion decoders
Diffusion decoders
SlotDiffusion'
17.8
50.4
55.3
44.9
49.3
SlotDiffusion'
37.2
31.0
35.0
31.2
36.5
SlotAdapt
29.6
51.5
51.9
Btable-LSDD
35.0
30.4
Ours
32.23
55,38
61.32
50.77
56.30
StotAdapi
41.4
35.1
39.2
36.1
41.4
Ours
47.54
36.61
41.43
36.41
42.60
5.2 PROPERTY PREDICTION
Following prior works (Dittadi et al., 2022; Locatello et al., 2020; Jiang et al., 2023), we evaluate the learned slot representations through downstream property prediction on the MOVi datasets. For each property, separate prediction network is trained using the frozen slot representations as input. We employ 2-layer MLP with a hidden dimension of 786 as the predictor, applied both categorical and continuous properties. Cross-ent Cross-entropy loss is used for categorical properties, while mean squared error (MSE) is used for continuous ones. assign object labels to slots, we use Hungarian matching between predicted slot masks and ground-truth foreground masks. This task evaluates whether slots encode object attributes in disentangled and predictive manner, beyond simply segmenting objects.
We report classification accuracy for categorical properties (Category) and MSE for continuous properties (Position and 3D Bounding Box), as shown in Table 3. With the exception 3D Bounding Box, CODA outperforms all baselines by significant margin. The lower performance on 3D bounding box prediction is likely due to DINOv2 features, which lack fine mme-grained geometric details necessary for precise 3D localization. Overall, these results indicate that the slots learned by CODA capture more informative and disentangled object features, leading to stronger downstream prediction performance. This suggests that CODA encodes properties that enable controllable compositional scene generation.
Table 3: Representation quality. Mean squared error (MSE) is reported for spatial attributes, including 'Position' and '3D bounding box", while classification accuracy is reported for "Category'. Results of other methods are taken from (Jiang al., 2023; Akan & Yemez, 2025).
MOVi-C
SLATE
SLATE*
LSD
Ours
MOVi-E
SLATE
SLATE
LSD
SlotAdapt
Ours
Position (L)
1.37
1.28
1.14
0.01
Position (L)
2.09
2.15
1.85
1.77
0.01
3D B- B-Box (1)
1.48
1.44
1.44
2.11
3D B-Box (L)
3.36
3.37
2.94
3.75
4.22
Category (1)
42.45
45.32
46.11
74.12
Category (1)
38.93
38.00
42.96
43.92
78.06
5.3
COMPOSITIONAL IMAGE GENERATION
To generate high-quality images, a model must not only encode objects faithfully into slots but also recombine them into novel configurations. We evaluate this capability through two tasks. First,
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assess reconstruction, which measures how accurately the model can recover the original input image. Second, we evaluate compositional generation, which tests whether slots can recombined into new, configurations. Following Wu et al. (2023), these configurations are created by randomly mixing slots within a batch. Both experiments are conducted on COCO. In our evaluation, we focus on image fidelity, since our primary goal is verify that slot-based compositions yield visually coherent generations. We report Fréchet Inception Distance (FID) (Heusel et al., 2017) and Kernel Inception Distance (KID) (Binkoossii et al., 2018) quantitative measures of image quality.
Table shows that CODA outperforms both LSD and SlotDiffusion, and further achieves higher fidelity than SlotAdapt. In the more challenging compositional generation setting, achieves the best results on both FID and KID, highlighting its effectiveness for slot-based composition. Beyond quantitative metrics, Figs. 3 and 12 demonstrates CODA's editing capabilities. By manipulating slots, the model can remove objects by discarding their corresponding slots replace them by swapping slots across scenes. These examples highlight that CODA supports fine- aeeeggraied, controllable edits in addition to faithful reconstructions, Overall, CODA not only preserves reconstruction quality but also significantly improves the ability of slot decoders to generalize compositionally, producing high-fidelityy images even in unseen configurations.
Table 4: Image generation results for reconstruction and compositional generalization the COCO dataset.
Results of other methods taken from (Akan & Yemez, 2025).
Reconstruction
Compositional generation
Metric
LSD
SlotDiffusion
SlotAdapt
Ours
LSD
SlotDiffusion
SlotAdapt
Ours
KIDx 10a
19.09 35.54
5.85 19.45
0.39 10.86
0.35
103.48 167.23
57.31 64.21
34.38
30.44
FID
10.65
40.57
31.03
Original image
Edited image
Target item
Replacement item
Edited image
Figure 3: Illustration of compositional editing. CODA can compose novel scenes from real-world images by removing (left) swapping (right) the slots, shown masked regions in the images.
5.4
ABLATION STUDIES
Image
CT
Baseline
+Reg
+CA
+CA+CO
+CA+Reg
CODA
Figure 4: Illustration of the ablation study VOC. We start from the pretrained diffusion model as slot decoder (Baseline), adding register slots (Reg), finetuning the key, value, and output projections the cross- attention layers (CA), adding contrastive alignment (CO).
We conduct ablations to evaluate the contribution of each component in our framework, with results on the VOC dataset summarized in Table 5. The baseline (first row) the frozen SD slot decoder. Finetuning the key, value, and output projections of the cross-attention layers (CA) yields moderate gains. Introducing register slots (Reg) provides substantial improvements, particularly in mBO, by

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reducing slot entanglement. Adding the contrastive loss (CO) further boosts mloU; however, applying without stopping gradients in the diffusion model (o) degrades performance. When combined, all components yield the best overall results, shown in the final row, with qualitative examples in Fig. 4. Further ablation studies on the COCO dataset are reported in Table and additional results provided in Section F. Overall, the ablations demonstrate that each component contributes complementary benefits in enhancing compositional slot representations.
Table 5: Ablation study on the VOC dataset
Component
Metric
Reg CA CO FG-ARIf mBO'
mBO' mloU" mloU"1
12.27
47.21
54.20
48.72
55.71
15.44
47.03
52.63
49.75
55.63
19.21
55.76
64.02
49.93
$7.14
11.96
47.16
54.17
49.40
56.56
56.27
65.05
50.40
58,02
15.48
47.95
53.72
51.80
57.98
31.27
$4,30
59
50.62
55.63
10.54
30.64
35.86
37.74
43.61
Figure 5: Segmentation masks learned by CCDA COCO
V
32.23
55.38
61.32
50.77
$6.30
6 CONCLUSIONS
We introduced CODA, diffusion-based OCL framework that augments slot sequences with input- independent register slots and a contrastive aligmment objective. Unlike prior approaches that rely solely denoising losses architectural biases, CODA explicitly encourages slot-image alignment, leading to stronger compositional generalization. Importantly, it requires no architectural modifications external supervision, yet achieves strong performance benchmarks, including COCO and VOC. Despite its current limitations (Section F), these results highlight the value of register slots and contrastive learning as powerful tools for advancing OCL.
synthetic and real- world
REPRODUCIBILITY STATEMENT
Section B.4 provides implementation details of CODA along with the hyperparameters used in experiments. All datasets used in this work are publicly available and can be accessed through their official repositories. To ensure full reproducibility, the source code and pretrained checkpoints available at https:// / /gi thub. com/ sony coda.
LLM USAGE
this work, large language models (LLMs) were used only to help with proofreading and enhancing the clarity of the text. All research ideas, thcoretical developments, experiments, and implementation were conducted entirely by the authors.
ETHICS STATEMENT
This work focuses on improving OCL and compositional image generation using pretrained diffusion models. While beneficial for controllable visual understanding, it carries risks: (i) misuse, as compositional generation could create misleading or harmful content; and (ii) bias propagation, since pretrained diffusion models may reflect biases in their training data, which can appear in generated images or representations. Our method is intended for research on OCL and representation, not for deployment in production systems without careful considerations.
ACKNOWLEDGMENTTS
The authors would like to thank Masato Ishii for helpful discussions on an earlier draft, We also thank anonymous reviewers for their constructive feedback and suggestions.
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Appendix
A Proofs
15
A.1 Proof of Theorem
15
A.2 Proof of Corollary
16
Experimental setup
16
B.1 Datasets
17
B.2 Basclines
Basclinos.......................
17
B.3 Metrics
17
B.4 Implementation details
18
Visualization attention scores
D
Compositional image generation from individual slots
19
E
Additional results
22
E.1
Classifier-free guidance
22
E.2
Learnable register slots
................................
22
E,3 Additional ahlation COCO
................................
23
Effect of the contrastive loss weighting
................................
23
E.S
Combination ratios for negative slots
23
E,6
Comparison with weakly-supervised baselines
24
E.7
Qualitative comparison
24
F
Limitations and future work
26
A PROOFS
A. PROOF OF THEOREM 1
To prove Theorem 1, build on theoretical results that connect data distributions with optimal denoising regression. Let define the MMSE estimator of e from noisy channel Zy, which mixes and noise level 7 as
E(2,, = Berp(ele,) = arg min Ep(e)p(e) [||e - E(z,, 7)1 a(x,,7)
Kong et al. (2023) showed that the log- likeeiill of z can be written solely in terms of the MMSE solution:
log logp(z) = 00
1
Er [||e El2,, d7
(6)
2
where - log(2ire) D 2 (r))dr is a constant independent of the data, with D denoting the dimensionality of z. Analogously, defining the optimal denoiser E(Zy, 7, s) for the conditional distribution p(z s) yields
1 / Eg [|le E(Z,, n, s) dy + c.
log s)
2
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Let $ q($ denote slots sampled from an auxiliary distribution ( z), which may differ from p(š z). Using the KL divergence, we obtain
DKL (g(5 2) z)) = (x(((())))))) log ( 2)
p(s z)
= (((x)) [log ( z) - log p(z S) log p(š) + log p(z)]
(+)))) [logp(e 5)] + (g z)]]p(()) + logp(z).
This leads to the following decomposition of the marginal distribution:
log p(z) = E (8 [l 6)] + DKL 2)([p( 2)) DeL (g($ x)((p(())
Consequently, the mutual information (MI) between z and s can be expressed as
T(z; s) F.p(a.)) [logp(z s)] Fyp()) [og](z))
Ep(x.s) [logp(z s)] Ep(x) loop(z s)]
PEL z)][p(š z)) (a(s z)]lp(())]
From Eq. (7), follows that
2 ((ss) Ele- s)]1 d7 L Eass) [|e - -((z,, 7, s)5)] dy
1
Ez (a(( 2) 2)) DKI(((( 2)) 2)][p(())]
1 00
(Bins), [|le E(Z,, 7, s) ((,,)) [||c E(Z,, 7, s)]1 dr
2
E2 [DKL(q($ z)]lp(5 2)) (g(( 2) 2))])
which completes the proof.
A.2 PROOF OF COROLLARY
Under the assumption that qi z) p()), it yields
Ez [g(((($ z)]]p(())] Eg [KL (p(5)]p(())
(9)
Similarly, the expected KL-divergence term in Eq. (8) simplifies as follows:
Ez [DKI((($ z)]lp($) x)) E, [Dri. (p(S) 2))]
p(s)
Ep(x)p(s)
log pts 2)
log p(z)p(5)
Bp(x)p(s)
p(s, z)
DKL (pL(p())(ss)]
(10)
Substituting Eqs. (9) and (10) into Eq. (8) completes the proof.
Remark 1. Eq. (10) shows that the additional expected Kl--divergence reduces to the reverse KL. divergence between the product of marginals p( p(z)p(() and the joint distribution piz. s). This term complements the standard mutual information 1 1(z; s), and together they form the Jeffreys divergence. Intuitively, while MI penalizes approximating the joint by the independent model, the reverse KL penalizes the opposite mismatch, thereby reinforcing the statistical dependence between z and
B EXPERIMENTAL SETUP
This section outlines the experimental setup of our study. We detail the datasets, baseline methods, evaluation metries, and implementation choices used in all experiments.
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B.1 DATASETS
MOVi-C/E (Greff et al., 2022). These two variants of the MOVi benchmark are generated with the Kubric simulator. Following prior works (Kakogeorgiou et al., 2024; Locatello et al., 2020; Seitzer al., 2023), we evaluate on the 6, 6.000-image validation set, since the official test sets are designed for out-of-distribution (OOD) evaluation. MOVi-C consists of complex objects and natural backgrounds, while MOVi-E includes scenes with a large numbers of objects (up 23) per image.
VOC (Everingham et al., 2010). We use the PASCAL VOC 2012 "trainaug" split, which includes 10,582 images: 1,464 images from the official train set and 9,118 images from the SDB dataset (Hari- haran et al., 2011). This configuration is consistent with prior works (Seitzer et al., 2023; Kakogeor- giou et al., 2024; Akan & Yemez, 2025). Training images are augmented with center cropping and then random horizontal flipping applied with probability of 0.5. For evaluation, we use the official segmentation validation set of 1,449 images, where unlabeled pixels are excluded from scoring.
COCO (Lin et al., 2014). For experiments, we use the COCO 2017 dataset, consisting of 118,287 training images and 5,000 validation images. Training images are augmented with center cropping followed by random horizontal flipping with probability 0.5. For evaluation, we follow standard practice (Wu et al., 2023; Seitzer et al., 2023) by excluding crowd instance annotations and ignoring pixels corresponding overlapping objects.
B.2 BASELINES
We compare CODA against state-of-the-art fully unsupervised OCL models. The baselines include SA (Locatello et al., 2020), DINOSAUR (Seitzer et al., 2023), SLATE (Singh et al., 2022a), SLATE* (a variant using pretrained VQGAN (Esser et al., 2021) instead of dVAE), SPOT (Kakogeorgiou et al., 2024), Stable-LSD' (Jiang et al., 2023) SlotDiffusion (Wu ct al., 2023), and SlotAdapt (Akan & Yemez, 2025). For DINOSAUR, we evaluate both MLP and autoregressive Transformer decoders. For SPOT, we report results with and without test-time permutation ensembling (SPOT w/ ENS, SPOT w/o ENS). We use the pretrained checkpoints released by the corresponding authors for SPOT and SlotDiffusion.
B.3 METRICS
Foreground Adjusted Rand Index (FG-ARI). The Adjusted Rand Index (Hubert & Arabic, 1985) (ARI) measures the similarity between two partitions by counting pairs of pixels that are consistently grouped together (or apart) in both segmentations. The score is adjusted for chance, with values ranging from 0 (random grouping) to (perfect agreement). The Foreground ARI (FG-ARI) is variant that evaluates agreement only on foreground pixels, excluding background regions.
Mean Intersection over Union (mIoU). The Intersection over Union (IoU) between predicted segmentation mask and its ground-truth counterpart is defined as the ratio of their intersection to their union. The mean IoU (mIoU) obtained by averaging these IoU values across all objects and images in the dataset. This metric measures how well the predicted segmentation masks overlap with the ground masks, aggregated over all instances.
Mean Best Overlap (mBO). The Best Overlap (BO) score for predicted segmentation mask is defined as the maximum IoU between that predicted mask and any ground-truth object mask in the image. The mean BO (mBO) is then computed by averaging these BO scores across all predicted masks in the dataset. Unlike mloU, which evaluates alignment with ground-truth objects directly, mBO emphasizes how well each predicted mask corresponds to its best-matching object, making less sensitive to under- or over-segmentation.
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Table 6: Hyperparameters used for CODA on MOVi-C, MOVI-E, VOC, and COCO datasets
Hyperparameter
MOVi-C
MOVi-E
VOC
COCO
General
Training steps
250k
250k
250k
500k
Learning rate
2 x 10-5
2 x 10-5
2 10-5
10-5
Batch size
32
32
32
32
Learning rate warm up
2500
2500
2500
2500
Optimizer
Adam W
AdamW
AdamW
AdamW
ViT architecture
DINOv2 VIT-B
DINOv2 ViT-B
DINOv2 ViT-B
DINOv2 ViT-B
Diffusion
SD v.1.5
SD v.1.5
SD v.1.5
SD v.1.5
Gradient norm clipping
1 0.03
Weighting Act
0.05
0.05
0.05
Image specification
Image size
512 Rand. Halid Full
512 Rand.HFlip Full
512 Kalip Central
512 HFlip Central
Augmentation
Crop
Slot attention
Input resolution
32
32
32 x 32
32 x 32
32 x 32
Number of slots Number of iterations
24
6
7
3 768
3
3
3
Slot size
768
768
768
B.4 IMPLEMENTATION DETAILS
The hyperparameters are summarized in Table We initialize the U-Net denoiser and VAE com- ponents from Stable Diffusion v1.5 (Rombach et al., 2022). During training, only the key, value, and output projections in the cross-attention layers are finetuned, while all other components remain frozen. For slot extraction, patch size of 14, producing feature maps of size 32 32. The input resolution is set to 512 x 512 for the diffusion model and 448 x 448 for SA. As form of data augmentation, horizontal flipping (Rand HFFlp) during training with probability of O.5. The negative slots are constructed by replacing 50% of the original slots with subset of slots sampled from other images within the batch. CODA is trained using the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 2 10-5 weight decay of 0.01, and a constant learning rate schedule with warm-up of 2500 steps. To improve efficiency and stability, we use 16-bit mixed precision and gradient norm clipping All models trained on 4 NVIDIA A A100 GPUs with local batch size of 32. We train for 500k steps on the COCO dataset and 250k steps on all other datasets. Training takes approximately 5.5 days for COCO and 2.7 days for the remaining datasets. For evaluation, the results are averaged over five random seeds. To ensure a fair comparision, for all FID and KID evaluations, we downsample CODA's 512 x 512 outputs to 256 x 256, matching the resolution used in prior works.
employ DINOv2 (Oquab et al., 2024) with a ViT-B backbone and
apply random
Attention masks for evaluation. We evaluate object segmentation using the attention masks produced by SA. At each slot iteration, attention scores are first computed using the standard sof tmax along the slot axis and then normalized via weighted mean:
((s("))((F) VD
(f)
m (1) softmax N
(8) mL
m
Σ
-
M
m 1m
(8)
where D denotes the dimension of k(f). The soft attention masks from the final iteration are converted to hard masks with argmax and used the predicted segmentation masks for evaluation. This procedure ensures that each pixel is assigned to the slot receiving the highest attention weight.
co/ stabl e- di f -osooonss-aallleeeeeeeoo e-di ffusion-v1-4 'https: :/tp thub. htttb/ccoo/acc/eooeeeee racebook
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C VISUALIZATION OF ATTENTION SCORES
We visualize the attention scores in Fig. 6, showing the average attention mass assigned to semantic slots versus register slots. CODA is trained on the COCO dataset, and illustrative images are randomly sampled. Since the cross-attention layers in SD are multi-head, we average the attention maps across both heads and noise levels.
Interestingly, although register slots are semantically empty, they consistently absorb a substantial portion of the attention mass. This arises from the sof tmax normalization, which forces attention scores to sum to one across all slots. When a query does not strongly correspond to any semantic slot, the model must still allocate its attention; register slots act neutral sinks that capture these residual values. This mechanism helps preserve clean associations between semantic slots and object concepts.
D COMPOSITIONAL IMAGE GENERATION FROM INDIVIDUAL SLOTS
We evaluate the ability of diffusion-based OCL methods generate images from individual slots. As shown in Fig. 7, each input image is decomposed into six slots, with each slot intended to represent a distinct concept. We then condition the decoder on individual slots to generate single-concept images. The last column shows reconstructions using all slots combined. While all methods can reconstruct the original images when conditioned on the full slot set, most fail to produce faithful generations from individual slots. Specifically, Stable-LSD (Jiang et al., 2023) produces mostly texture-like patterns that poorly match the intended concepts, while SlotDiffusion (Wu et al., 2023), despite being trained end-to-end, also struggles to generate coherent objects. In Stable-LSD, slots are jointly trained to reconstruct the full scene, so object information can be distributed across multiple slots rather than concentrated in any single one. Consequently, removing all but one slot at test time puts the model in an out- of-distribution regime, and single-slot generations do not yield coherent objects even though the full slot set reconstructs the image well. This reflects slot entanglement where individual slots mix features from multiple objects. SlotAdapt (Akan & Yemez, 2025) partially alleviates this issue through an average register token, but since their embedding is injected directly into the cross-attention layers and tied to input-specific features, it limits flexibility in reusing slots across arbitrary compositions. In contrast, the ceput-independeri register slots introduced in CODA act as residual sinks and do not encode input-specific features, enabling more faithful single-slot generations and greater compositional flexibility.
To quantify these results, we report FID and KID scores by comparing single-slot generations against the real images in the training set. For each validation image, we extract six slots and generate six corresponding single-slot images, ensuring fair comparison across methods. Results on the VOC dataset are reported in Table 10, where CODA achieves the best scores, confirming its ability to generate coherent and semantically faithful images from individual slots.
Although register slots substantially reduce background entanglement, they do not enforce a hard separation between foreground and background. The attention mechanism in SA still remains soft, and our objectives do not explicitly prevent semantic slots from attending to background regions. As result, semantic slots may still absorb contextual pixels, especially near object boundaries in textured areas that are useful for reconstruction, when the number of slots exceeds the number of objects. As result, small "meaningless" background fragments may still be assigned semantic slots, as seen in Fig. 7. Empirically, however, we find that register slots substantially decrease background leakage compared to baselines without registers.
Table 7: Image generalization quality when using individual slots on the VOC dataset
Metric
Stable-LSD
SlotDiffusion
SlotAdapt
Ours
KID x x FIDD
111.30 189.77
23.26
10.86
5.09
94.88
47.70
27.61
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Slot
Slot
Register slots
Eayer
Layer'
Layer
Layer
Layer
Layer 5
Layer 7
Layar
Layer
Layar
Layer 11
Layer 12
Layer
Layer
Lager Layer
Figure 6: Attention scores across different cross-attention layers, averaged over heads and noise levels. The first column shows the original input image fed to CODA. Each image row Layer and column Slot visualizes the total attention mass assigned to slot j at layer i. The last column reports the total attention mass absorbed by the register slots. CODA heavily attends to the register slots across all layers.
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SlotDiffusion
Stable-LSD
SlotAdapt
ours
Figure 7: Image generation from individual slots. For each method, Top: slot masks, Bottom: generated images. The last column shows reconstructions from all slots. In CODA, register slots can be regarded as part of the U-Net architecture as they are independent from the input. Compared to baselines, our method generates faithful images from individual slots.
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E ADDITIONAL RESULTS
In this section, we present supplementary quantitative and qualitative results that provide further insights into the performance of CODA.
E. (LASSIFIER-FRES GUIDANCE
To enhance image generation quality, employ classifier-free guidance (CFG) (Ho & Salimans, 2021), which interpolates between conditional and unconditional diffusion predictions. A guidance scale of CFG 1 corresponds on different CFG values to assess their impact generation quality. As shown in Fig. 8, both FID (Heusel al., 2017) and KID (Binkowski et al., 2018) scores improve with moderate guidance. with CODA achieving the best performance at CFG = 2.0. This indicates that balanced level of guidance enhances fidelity without over-amllifiing artifacts.
standard conditional generation. We conduct ablation study
1e-3
13
1.5
1.0
CFG
CFG
Figure 8: Generation fidelity on the COCO dataset for different CFG values
E.2 LEARNABLE REGISTER SLOTS
Several works have explored trainable tokens as auxiliary inputs to transformers. For example, the [CLS] token is commonly introduced for classification in VIT (Dosovitskiy et al., 2021) and BERT (Devlin et al., 2019), while CLIP (Radford et al., 2021) employs an [EOS] token. These tokens serve as learnable registers that allow the model store and retrieve intermediate information during inference. Goyal et al. (2024) demonstrated that appending such tokens can boost performance by increasing token interactions, thereby promoting deeper computation. Similarly, Darcet al. (2024) utilized register tokens during pretraining to mitigate the emergence of high-norm artifacts. Motivated by these findings, we experiment with replacing our frozen CLIP-derived register slots with learnable ones. These slots are appended to the slot sequence but remain context-free placeholders.
Results on VOC with varying numbers of learnable register slots are shown in Table 8. The model without register slots (R 0) performs the worst across all metrics (FG-ARI, mBO', mBO mloU', and mloU%). Interestingly, introducing just a single register slot leads to a significant performance boost. Further increasing the number of tokens R 77, matching the configuration used in CODA, yields only marginal improvements. Although more register slots could slightly increase computational cost, this is negligible the number of register slots is relatively small. For instance, using 77 register slots increases GPU time by only 0.02% compared to the baseline without using any register slot. Interestingly, CODA achieves the best performance when using frozen register slots. These findings emphasize the effectiveness of register slots in improving the model performance.
Table 8: Ablation study varying the number of register slots on the VOC dataset
R
FG-ARIt
mBO't
mB0
m
mloU"*
0
15.44
47.03
52.63
49.75
55.63
I
30.39
54.47
59,96
50.21
55.34
4
29.89
54.91
60.15
50.65
55.65
64
30.40
54.62
59.93
50.47
55.45
77
30.21
55.26
60.89
50.86
56.07
CODA
32.23
55.38
61.32
50.77
56.30
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E.3 ADDITIONAL ABLATION ON COCO
We further examine the contribution of frozen register slots on the COCO dataset, using pretrained SD as the slot decoder baseline. Different settings are evaluated: (i) adding register slots (+Reg), (ii) adding register slots combined with finetuning the key, value, and output projections in cross-attention layers (+CA), and adding the contrastive loss (+CO). As shown in Table 9, register slots consistently improve performance in both cases, demonstrating their robustness and effectiveness when integrated into the slot sequence.
Table 9: Ablation study on the COCO dataset
Method
FG-ARI
mBO't
mBO
mloU'1
mloU**
Baseline
20.99
29.77
37.21
32.16
41.25
Baseline Reg
23.64
31.14
39.07
32.64
41,91
Baseline CA
36.99
33.82
38.08
35.04
41.24
Baseline CA Reg
45.95
35.80
40.32
35.76
41.75
Baseline CO
25.24
30.14
38.77
32.83
42.99
Baseline CO CA
35.84
34.36
38.67
36.28
42,85
Baseline CA Reg CO (CODA)
47.54
36.61
41.43
36.41
42.64
We further analyze the effect of the contrastive loss on image generation. Results are reported in Table 10. Without the contrastive loss, Reg+CA achieves slightly better FID/KII under compo- sitional generation than the full model Reg+CA+CO. This aligns with the role of CO, which is primarily intended to strengthen slot-image alignment and object-centric representations rather than to maximize image fidelity, and can therefore marginally degrade FID/KID. Overall, CO should be viewed as an optional component that further improve object discovery at a small cost visual quality.
Table 10: Image generation results for reconstruction and compositional generalization on the COCO dataset
Reconstruction
Compositional generation
Metric
Reg + CA
Reg CA + CO
Reg CA
Reg CA CO
KIDx103 FID
0.39
0.35
27.95 29.34
30.44
10.65
10.65
31.03
E.4 EFFECT OF THE CONTRASTIVE LOSS WEIGHTING
We conduct ablation study analyze the impact of the weighting coefficient Àal in the contrastive loss term of our objective function in Eq. (3). Results on the COCO dataset are shown in Table 11. The study reveals that moderate values of Ael achieve the best trade-off between the denoising and contrastive objectives, yielding the strongest overall performance. In practice, very small weights underuse the contrastive signal, while excessively large weights destabilize training and harm reconstruction quality. Although the contrastive loss shares a similar form with the diffusion loss, in practice, we find that needs to be weighted by a relatively small factor Xet to obtain good results. Empirically, increasing Aol consistently degrades visual quality. We hypothesize that this happens because the contrastive term operates on slot-level features and, when heavily weighted, over-emphasizes alignment the expense of the diffusion prior, leading overspecialized and less realistic samples. In contrast, a small del acts as a weak regularizer that improves alignment while keeping the diffusion objective dominant.
E.5 COMBINATION RATIOS FOR NEGATIVE SLOTS
This section explores different combination ratios for constructing negative slots s. As outlined in Section 4.3, given two slot sequences s and from two distinct images x and x', we randomly replace fraction (0, of slots from with those from s'. When = 1, the entire set of slots
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Table 11: Ablation study on varying the weighting terms in contrastive loss on the COCO dataset
Xet
FG-ARIT
mB0't
mBO
mloU*t
mloUst
45.95
35.80
40.32
35.76
41.75
0.001
46.07
35.99
40.50
36.18
42.18
0.002
45.93
35.88
40.79
35.74
42.02
0.003
47.54
36.61
41.43
36.41
42.60
0.004
46.87
36.38
41.13
36.26
42.41
0.005
44.98
35.80
40.86
35.44
41.75
is replaced by s', while values < < 1 yield mixed sets of slots 8 that only partially mismatch the original slots s. Results VOC (Table 12) show that 0.5 performs best, whereas r leads to overly trivial negative slots that provide little gradient signal. Intuitively, partial mismatches act as harder negatives, forcing the model to better discriminate correct slot-image alignments.
Table 12: Ablation study on varying the portion of negative slots on the VOC dataset
FG-ARIT
mBO't
mB0
mloU mloU't
0.25
32.34
54.58
60.52
50.44
55.97
0.50
32,23
55.38
61.32
50.77
56,30
0.75
33.34
55.06
60.98
50.12
55.61
1.00
32.67
54.60
59.84
49.73
$4.64
E.6 COMPARISON WITH WEAKLY-SUPERVISEE BASELINES
We compare CODA to GLASS (Singh et al., 2025), weakly supervised approach that uses guidance module to produce semantic masks as pseudo ground truth. In particular, BLIP-2 (Li et al., 2023) is used for caption generation to create guidance signals. While this supervision helps GLASS mitigate over-segmentation, also limits its applicability in fully unsupervised settings. contrast, CODA does not rely on any external supervision and can distinguish between multiple instances of the same class, enabling more fine-grained object separation and richer compositional editing.
Table 13 reports the results. We additionally consider GLASS", variant of GLASS that uses ground- truth class labels associated with the input image. While GLASS achieves stronger performance on semantic segmentation masks, it underperforms CODA object discovery, as reflected by lower FG-ARI scores. This suggests that CODA is better at disentangling distinct object instances at a conceptual level.
Table 13: Unsupervised object segmentation comparison with weakly-supervised OCL on real- world datasets, including VOC (left) and COCO (right). The results of GLASS and GLASS are taken from (Singh et al., 2025).
VOC
FG-ARIT
mBO't mBO mloU'
COCO
FG-ARI
mBO't
mBO
mloU'
GLASS
21.3
58.5
61.5
57.8
GLASS! GLASS
32.5
40.8
48.7
39.0
GLASS
22.5
58.9
62.2
58.1
34.1
40.6
48.5
38.9
Ours
32.23
55.38
61.32
50.77
Ours
47.54
36.61
41.43
36.41
E.7
QUALITATIVE COMPARISON
To complement the quantitative results in the main paper, present additional qualitative examples that illustrate the effectiveness of CODA. These examples provide a more complete picture of the model's performance and highlight its advantages over previous approaches.
Object segmentation. We visualize segmentation results in Fig. 9. CODA consistently discovers objects and identifies semantically meaningful regions in fully unsupervised manner. Compared to
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diffusion-basee based OCL baselines such as Stable-LSD and SlotDiffusion, CODA produces cleaner masks with fewer fragmented segments, leading to more coherent object boundaries.
Image
GT
Stable-LSD
SlatDiffusion
SPOT
SlotAdapt
Ours
Figure 9: Visualization of image segmentation results on the COCO dataset. Compared to other methods, our method tends to produce more accurate masks with fewer fragmented segments.
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Reconstruction. Figs. 10 and 11 show reconstructed images generated by CODA. The results demonstrate that CCDA produces high-quality reconstructions when conditioned on the learned slots. Importantly, the generated images preserve semantic consistency while exhibiting visual diversity, indicating that the slots capture abstract and meaningful representations of the objects in the scene.
Compositional generation. Fig. 12 showcases COCO image edits based CODA's learned slots, including object removal, replacement, addition, and background modification. We find that the editing operations highly successful, introducing only minor adjustments while consistently preserving high image quality.
F LIMITATIONS AND FUTURE WORK
While CODA achieves strong performance across synthetic and real-world benchmarks, it has several limitations that open avenues for future research. (i) CODA relies on DINOv2 features and SD backbones, which may inherit dataset-specific biases and limit generalization to domains with very different visual statistics.
(ii) While our contrastive loss improves slot-image alignment, full disentanglement in cluttered or ambiguous scenes remains open challenge. (iii) Inherited from SA, CODA still requires the number of slots be specified in advance. This restricts flexibility in scenes with a variable or unknown number of objects, and can lead to either unused slots missed objects (Fan et al., 2024). In our implementation, CODA uses a fixed number of semantic slots plus small number of register slots. Note that the register slots do not reduce semantic capacity but also cannot resolve the fundamental bottleneck when the true number of objects exceeds the available semantic slots, in which background entanglement. This is because the contrastive alignment is defined only for semantic slots, which encourages them explain object-level content, while register slots are discouraged from encoding object-like structure.
objects may still be merged into the same slot despite reduced
Despite our improvements in object discovery and compositional control, faithfully preserving fine- grained images in reconstructions and compositional edits remains challenging, as also observed in prior slot diffusion models (e.g.. SlotAdapt). We attribute this to several factors: (i) slot representations act as a low-dimensional bottleneck that must compress both geometry and detailed appearance; (ii) the diffusion backbone is pretrained to model images (and text-image pairs) but not to decode from slot-based object latents; and (iii) our training objective emphasizes object-centric grouping and controllability rather than exact pixel-level reconstruction. Improving image reconstruction in OCL is an important direction for future work.
Although CODA is conceptually compatible with a wide range of diffusion backbones, this work restrict ourselves to a relatively small, widely used backbone to ensure fair comparison with prior object-centric methods (c.g.. SlotAdapt, LSD) and to keep computational and memory require- ments manageable. We do not explore scaling CODA to larger, more recent architectures such as SDXL (Podell et al., 2024) or FLUX (Labs, 2024), which would require substantially more resources and additional engineering effort handle larger feature maps, model sizes, and more sophisticated lexi-conditioning pipelines (c.g., multiple text encoders and auxiliary pooled text embeddings). De- spite these limitations, we believe that CODA offers scalable and conceptually simple foundation for advancing OCL. A promising direction for future work is extending CODA to Diffusion Transformers (DiTs) (Peebles Xie, 2023), where slot representations could naturally replace or complement text embeddings in cross-attention, enabling richer and more flexible compositional control, as well as investigating integrations with larger backbones such as SDXL/FLUX to more fully assess the generality of our approach.
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SlotAdapt
ours
GT
SlotAdapt
Ours
(a) VOC
GT
StotAdapt
Ours
GT
SlotAdapt
Ours
SMA
(b) COCO
Figure 10: Reconstructed images on real-world datasets. Top: ground-truth (GT) images. Middle:
images reconstructed by SlotAdapt. Bottom: images reconstructed by CODA.
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Ours
GT
ours
MOVi-C
GT
Ours
ours
(b) MOVi-E
Figure 11: Reconstructed images on synthetic datasets. Top: ground -tuth (GT) images. Bottom: images reconstructed by CODA.
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Original image
Remove item
Edited image
Target item
Replacement item
imuge
Replacement bac Background
Original imuge
Add item
Edited imuge
Target background
Edited image
Figure 12: Illustration of compositional editing. CODA composes novel scenes from real-world images by removing (top left), swapping (top right), and adding (bottom left) slots, well changing the background (bottom right). The masked objects indicate the slots that are added, removed, or replaced relative the original image.
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Reviews:
[{"review_id": "vyo4BuKLDa", "reviewer": "Reviewer_hjxk", "summary": "The paper addresses the issue of binding of objects to slots in existing slot-based OCL methods. It proposes using register tokens/slots and a contrastive loss to alleviate this issue, and also fine-tunes the query/key embeddings in the diffusion model to better adapt it to the slot representation. The effectiveness of the proposed solution is evaluated on standard datasets in terms of various tasks, including object discovery, property prediction, and compositional generation. Furthermore, detailed ablations demonstrate the benefit of (fixed) slot registers and the contrastive loss. The paper discuss in detail how these components contribute to the performance gain and provides an additional mathematical justification for the contrastive loss.", "questions": "1. Can the authors discuss the effect of the method vs. the number of objects in the scene? For example, if a given image has a 7 objects for 7 slots, does adding register tokens hurt the performance in this case? What happens if the number of object tokens is greater than 7?\n2. How did the authors settle on the ‘odd’ number of 77 register tokens? Where does this number come from? Also, does this large number of register tokens affect computational efficiency?\n3. Can the authors discuss why the contrastive loss guidance scale needs to set to a really low value for good results?\n4. For figure 7, it seems like the slots from StableLSD do not correspond/encode the object at all (we are not able to reconstruct the object from individual slot), however, the joint slot is able to reconstruct the scene fairly well. Why do you think this is case?\n5. Is it really the case that fine-tuning the cross-attention mechanism (CA) does not reduce the expressive power of the model (l. 228)? How can we assure that this is indeed the case?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "* (S1) The paper is very well written, clearly explains the shortcomings of existing methods and motivates the proposed solutions. The methodology section is well-explained.\n* (S2) Though the ideas in the paper can be seen as a combination of several ideas from existing works (register tokens [1], negative guidance [2], contrastive learning of slots [3]), the paper creatively combine these ideas in the context of slot attention methods. This enhances the technical contributions of the paper.\n* (S3) The results for different tasks, from property prediction, object discovery, and compositional generation, clearly show that the method outperforms previous methods by a large margin. The improvement for categorical prediction is particularly impressive. Additionally, the paper provides detailed ablations for several configuration settings, including the number of register tokens and the scale of the CFG guidance weight.\n* (S4) The results in Figure 7 are impressive, indicating that the slots actually bind to the objects and that the model is able to reconstruct the objects to a surprising degree with these slots. This highlights that the approach indeed allows to better disentangle the slot representations.\n\nReferences:\n* [1] Darcet et al., Vision Transformers Need Registers, ICLR 2024.\n* [2] Karras et al., Guiding a Diffusion Model with a Bad Version of Itself, NeurIPS 2024.\n* [3] Manasayan et al., Temporally Consistent Object-Centric Learning by Contrasting Slots, CVPR 2025.", "weaknesses": "* (W1) Fairness of evaluation — A significant issue in the comparison with other methods is the use of DINOv2 and the 512x512 image size for the training method. Other methods, such as SPOT and DINOSAUR, use a DINOv1 model with an image size much smaller than 512x512. Thus, the proposed gains in the method cannot be attributed solely to the slot attention registers and constructive loss.\n* (W2) Ablations on the VOC dataset — Though I understand that performing the ablations on VOC is quicker, the VOC dataset is much simpler compared to the COCO dataset (many images consist of only a single object), and to thoroughly gain insights on the effect of the proposed changes, the paper should conduct the ablations on a more complex dataset like COCO (even if the ablations are done over a subset of the COCO dataset).\n* (W3) The need for training of cross-attention (CA) layers is not clearly explained by Tab. 5. FG-ARI as a metric has been criticized by several papers (SPOT, GLASS), etc. The primary motivation for training the CA layers appears to be achieving a good reconstruction output rather than improving object discovery metrics. The paper should refer to Table 9 in the appendix to clarify the contribution of training CA layers.\n\nMinor points:\n1. Figure 2, lower branch should be $\\epsilon_\\bar{\\theta}$ instead of $\\epsilon_\\{\\theta}$.\n2. Figure 2: DINO should be DINOv2.\n3. Figure 6 is hard to understand due to the use of different input images with different layers. The figure should be supported with a more illustrative caption.\n4. It would be useful if the paper were to provide more qualitative results for compositional generation.\n5. In l. 236, the paper claims that $\\mathbf{s}$ are DINOv2 features, but these are actually the slots computed from DINOv2 features.\n6. Reference [2], see above, is missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TryhBxxqga", "reviewer": "Reviewer_vEFV", "summary": "This paper proposes Contrastive Object-centric Diffusion Alignment (CODA), an object-centric learning (OCL) framework with Slot-Attention and a pre-trained Stable Diffusion (SD) decoder. Prior work SlotAdapt already shows that using a powerful pre-trained SD model can greatly improve the generation quality of the OCL method. CODA further strengthens the results in three ways: 1) register slots (empty slots) to absorb attention on non-object areas to improve slot disentanglement, 2) an easier approach adapting the pre-trained text-conditioned cross-attention layer to condition on slots, and 3) contrastive alignment that maximizes the reconstruction loss when conditioned on unrelated slots. With these techniques, CODA outperforms baselines in both segmentation and generation on real-world datasets.", "questions": "See weaknesses. Some minor questions not affecting my decision:\n1. Have you tried SD 2.1? In my experience, SD 2.1 has a similar model size / arch as SD 1.5, but often performs better in generation, likely due to its v-predicition formulation.\n2. What do you think is still missing in achieving better reconstruction of input images from slots? Checking results in Fig.10, the reconstructed object's identity is still clearly not preserved. I understand this is also the case for SlotAdapt so I don't view it as a weakness of the paper.\n3. In the qualitative segmentation results in Fig.7, why are there still some meaningless background pixels being segmented to a slot? E.g., column 1, 2 in the train example, they don't seem to correspond to any objects. In theory, they should be absorbed by the register slots?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- In this era of foundation models, I always believe the community of OCL should move to more pre-trained models. This paper is a nice attempt at using DINO and SD models, and demonstrates effectiveness at large-scale real-world datasets.\n- All three techniques in CODA are well-motivated and implemented. I appreciate the thorough ablations in the main paper and the Appendix. They help answer the effectiveness of each component very well.\n- The analysis of mutual information (MI) is interesting and inspiring.", "weaknesses": "I don't see big weaknesses in the paper. Some minor weaknesses:\n1. Each component is not very novel, as they pre-exist in other areas. Though I don't view this as a big issue -- combining them in a nice way and achieve strong results is also a good contribution.\n2. Why not comparing with GLASS in the experiments? It seems that the segmentation results are similar to them (by checking tables in their paper), but I believe the generation capability of CODA must be stronger. Please include this in the paper.\n3. I don't see enough qualitative generation comparison with SlotAdapt. While the quantitative results of CODA clearly outperform it, it's always beneficial to see some visual results. For example, I would like to see SlotAdapt results in Fig.9 and Fig.10 in the Appendix. Is it because they haven't released their code? There is a `Code` button on their webpage but it doesn't seem to lead to anywhere. Then how do you get SlotAdapt in Fig.7 of Appendix?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AjSRK2BoEe", "reviewer": "Reviewer_73eg", "summary": "The paper presents a method for object-centric diffusion learning that extends Object-Centric Slot Diffusion (LSD) [1] by introducing two additional mechanisms: (i) register slots, designed to act as attention sinks, and (ii) a contrastive alignment loss to reduce slot entanglement. The model fine-tunes key and value projections in the cross-attention layers of a frozen Stable Diffusion backbone, while keeping the rest of the weights fixed.\nThe claimed novelty of register slots is, however, questionable. The concept was originally proposed in SlotAdapt [2], which introduced a separate learnable slot that serves exactly the same purpose. As also shown in Table 9 of the appendix, the authors acknowledge this similarity. The only difference is that in the current paper, the register slots are frozen rather than learnable, a minor technical modification rather than a conceptual contribution.\nThe proposed contrastive loss closely follows Learning to Compose [3], where compositional consistency is enforced through a shared slot initialization across samples. This paper adopts the same shared initialization trick, and the resulting contrastive objective is largely an adaptation rather than a new formulation.\n\n[1] Jiang et al., LSD, NeurIPS 2023\n[2] Akan & Yemez, SlotAdapt, ICLR 2025\n[3] Jung et al., Learning to Compose, ICLR 2024", "questions": "- The paper reports diffusion generation at 512×512 resolution, while most prior work (e.g., SlotAdapt, LSD) uses 256×256. How were comparisons conducted under this mismatch? Higher resolution can directly improve image quality metrics such as FID and KID.\n- The training iterations are inconsistent across methods. SlotAdapt trains for 200K (MOVi-E), 250K (COCO), and 190K (VOC), while CODA uses 500K for COCO and 250K for VOC. Comparisons would be more meaningful if conducted under the same training budget.\n- The paper would benefit from additional qualitative results on compositional editing to more clearly demonstrate improvements in visual plausibility and slot-level control.\n- In Figure 7 (Appendix), it is unclear whether the single-slot generations include register slots. That is, whether the output is generated from one semantic slot plus registers or solely from the selected slot.\n- On Table 3 results, is MOVi-E position prediction really correct? 0.01 looks like an anomaly.\n- Results are reported for single runs, but given the stochastic nature of slot initialization, multi-seed evaluations would provide stronger statistical support.\n- Since the proposed method is relatively lightweight, it could be scaled to larger backbones such as SDXL or FLUX variants to test its generality. No prior object-centric work appears to have explored this yet.\n- An ablation on the classifier-free guidance (CFG) parameter, similar to that reported in SlotAdapt (Table 10, Appendix), would help analyze the effect of guidance strength.\n\nMinor typo: Fig. 3 caption, \"Orginal image\" -> \"Original image\".", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The method reports state-of-the-art results on both unsupervised object segmentation and compositional generation benchmarks, though the fairness of the compositional generation evaluation is debatable (see Weaknesses first and last point).\n- The idea is conceptually simple, it does not introduce any architectural changes. (Although new register tokens can cost in terms of runtime (mostly negligible I guess, as the paper states that it only costs 0.02%) and contrastive loss is actually 2x forward (but in training))\n- The experimental setup is solid, with comprehensive evaluations across multiple datasets and informative supplementary analyses in the appendix.", "weaknesses": "- The compositional generation evaluation is not entirely fair. The proposed method (CODA) is explicitly trained to reconstruct images given random slot combinations through its contrastive objective, which involves positive and negative slot pairs. This directly optimizes the model for composition-like reconstruction, so improved performance under this metric is somewhat expected. The qualitative results, however, are convincing and indicate stronger disentanglement than SlotAdapt, likely due to reduced slot entanglement from contrastive alignment.\n- The paper’s claim that it \"introduces\" register slots is inaccurate. SlotAdapt (Akan & Yemez) already proposed a similar concept (see Table 9 in its appendix). While the current implementation with frozen register slots inspired by attention sinks in LLMs can be considered an improved version, it does not constitute a new contribution and should be framed as such.\n- The contrastive loss significantly increases training cost, requiring 2× forward passes. 1x forward with 1/2 batch size is not possible because of the gradient stopping for the diffusion model. \n- The ablation results are ambiguous. The combination of register slots and contrastive alignment (Reg+CA) already achieves nearly all the reported gains on COCO and VOC. The additional contrastive loss provides only marginal quantitative improvements despite its high computational cost. My guess is that this component mainly benefits compositional generation, since the contrastive objective explicitly encourages the model to reconstruct plausible images under slot mixing. If that is the case, it would be important to show both quantitative and qualitative results for compositional generation using the Reg+CA model (without CO) to confirm whether the improvement truly stems from better compositionality or merely from training bias toward the contrastive task.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)