CGSA: Class-Guided Slot-Aware Adaptation for Source-Free Object Detection
Authors: Boyang Dai
Zeng Fan
Zihao Qi
Meng Lou
Yizhou Yu
Venue: ICLR 2026 Poster (2026)
Keywords: Source-Free Domain Adaptation
Object Detection
Object-Centric Learning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Source-Free Domain Adaptive Object Detection (SF-DAOD) aims to adapt a detector trained on a labeled source domain to an unlabeled target domain without retaining any source data. Despite recent progress, most popular approaches focus on tuning pseudo-label thresholds or refining the teacher-student framework, while overlooking object-level structural cues within cross-domain data. In this work, we present CGSA, the first framework that brings Object-Centric Learning (OCL) into SF-DAOD by integrating slot-aware adaptation into the DETR-based detector. Specifically, our approach integrates a Hierarchical Slot Awareness (HSA) module into the detector to progressively disentangle images into slot representations that act as visual priors. These slots are then guided toward class semantics via a Class-Guided Slot Contrast (CGSC) module, maintaining semantic consistency and prompting domain-invariant adaptation. Extensive experiments on multiple cross-domain datasets demonstrate that our approach outperforms previous SF-DAOD methods, with theoretical derivations and experimental analysis further demonstrating the effectiveness of the proposed components and the framework, thereby indicating the promise of object-centric design in privacy-sensitive adaptation scenarios. Code is released at https://github.com/Michael-McQueen/CGSA.

Published as a conference paper at ICLR 2026
CGSA: CLASS-GUIDER SLOT-AWARE ADAPTATION FOR SOURCE-FREE OBJECT DETECTION
Boyang D Zeng Fan?: Zihao Qi Meng Lou', Yizhou Yu
School of Computing and Data Science, The University of Hong Kong
2 *Department of Electrical Computer Engineering. National University of Singapore
School of Information and Electronics, Beijing Institute of Technology
*Center for Embodied Al and Computer Vision, Shenzhen Loop Area Institute
{
boyangdai@conneett hku. hk, e152114 48uu. nus . edu,, 31202556018bit. www.ebiitteec..om...
comeng@connect.. hku. hk, zhouy@acm.org
ABSTRACT
Source-Free Domain Adaptive Object Detection (SF-DAOD) aims adapt a de- tector trained on a labeled source domain to an unlabeled target domain without retaining any source data. Despite recent progress, most popular approaches fo- cus on tuning pseudo-label thresholds or refining the teacher-student framework, while overlooking object-level structural cues within cross-domain data. In this work, we present CGSA, the first framework that brings Object-Centric Learn- ing (OCL) into SF-DAOD by integrating slot-aware adaptation into the DETR- based detector. Specifically, our approach integrates a Hierarchical Slot Aware- ness (HSA) module into the detector to progressively disentangle images into slot representations that act visual priors. These slots are then guided to- ward class semantics via a Class-Guided Slot Contrast (CGSC) module, main- taining semantic consistency and prompting domain-invariant adaptation. Ex- tensive experiments on multiple cross-domain datasets demonstrate that our ap- proach outperforms previous SF-DAOD methods, with theoretical derivations and experimental analysis further demonstrating the effectiveness of the pro- posed components and the framework, thereby indicating the promise of object- centric design in privacy-sensitive sensitive adaptation scenarios. Code is released at https: / thub. com /ii chael -McQueen/CGS.
INTRODUCTION
Real-world deployment of object detec- tors (Ren al., 2015; Redmon et al., 2016; Carion et al., 2020) inevitably encounters domain shifts, such as changes in weather, camera, or scene layout, which cause dra- matic performance drops when the model is trained on different, labeled source do- main. Unsupervised domain-adaptive object detection (DAOD) addresses this gap by adapting a detector to an unlabeled target domain while still having full access to the source data. Existing DAOD methods pre- dominantly align global or instance features through adversarial learning (Chen et al., 2018), query-token matching (Huang al., 2022), or teacher-student consistency (Li et al., 2022b). Although these techniques yield solid
Source Pretraining
Target Adaptation
Source Pretraining
Target Adaptation
Bi >
*< 5. >
< Si
*< Sh >
Mode
(c] Popular Approach
Our Approach
Figure I: Motivation of the proposed CGSA. (a) Most popular methods focus on filtering pseudo labels. (b) Our CGSA proposes slot-aware framework serve as bridge for aligning com- mon object-level structural features without ac- cessing source data.
"qqual contribution. Corresponding author.

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Published as a conference paper at ICLR 2026
gains, they presuppose that source images remain available during adaptation, assumption often violated by privacy regulations or proprietary constraints.
In Source-Free DAOD (SF-DAOD), this dependency is relaxed because the adaptation phase ceives only a detector pretrained on the source domain and the target images themselves. Be- cause source data are unavailable and target labels are absent, SF-DAOD typically adopts teacher-student paradigm (Tarvainen & Valpola, 2017): the teacher predicts on target images, fil- tered predictions form pseudo labels, and the student is trained on these signals. Consequently, current approaches focused on refining the framework or the selection scheme, such as confidence filtering (Li et al., 2021), periodic smoothing (Liu et al.. 2023), and energy-based pruning (Chu et al., 2023). Despite recent progress, popular methods largely ignore the object-level structural regularities that persist across source and target domains. As result, the source-trained detector relegated to a mere pseudo-label oracle, and its rich internal representations remain under-exploited.
In response the above challenges, Object-Centria Learning (OCL) offers a solution. Slot Atten- tion (Locatello et al., 2020), representative OCL technique, decomposes scene into small set of latent "slots", each expected to bind to one object and thus inherently separate foreground entities from background context. Slot-based models have shown impressive transferability in many vision tasks (Seitzer et al., 2022: Zhou et al., 2022; Jiang et al., 2023), yet have never been explored for SF-DAOD. Given that DETR-base based detectors (Carion et al., 2020) already rely object queries, embedding slot-aware priors into the query space is a natural but unexplored direction.
Consequently, propose CGSA, the first framework merge OCL with source-free adaptive de- tection. Figure/ shows our motivation. CGSA acquires slot-based structural visual priors through progressive visual decomposition mechanism, and guides the resulting slots with class prototypes toward domain-invariant representations via contrastive learning that evolves as the detector im- proves. Together, by explicitly extracting, guiding, and leveraging these shared structural cues, our framework allows the pretrained model to contribute more directly and effectively during adaptation.
The main contributions are summarized as follows:
(i)
To the best of our knowledge, we are the first to introduce OCL into the SF-DAOD problem, establishing new slot-aware adaptation framework CGSA.
iii) We devise two complementary modules, namely Hierarchical Slot Awareness (HSA), Class- Guided Slot Contrast (CGSC), which collectively provide structural visual priors and se- mantic guidance to exploit domain-invarian slot awareness under the source-free setting. We further provide a theoretical generalization analysis supporting our method.
(iii) Extensive experiments on multiple datasets, complemented by ablation studies and visualiza- tion analyses, demonstrate the effectiveness of the proposed components and framework.
2 RELATED WORKS
Unsupervised Domain Adaptive Object Detection (DAOD) aims to transfer a detector trained on labeled source domain an unlabeled target domain. Early works extend feature-level adversarial learning from classification to detection. For example, DA Faster R-CNN (Chen et al., 2018) aligns image-level and instance-level features with two discriminators. With the advent of transformer detectors, query-aware alignment becomes feasible. MTTrans (Yu et al., 2022) and AQT (Huang al., 2022) match cross-domain tokens or queries via the attention mechanism. DATR (Chen et al., 2025) further couples alignment across classes. The third technical route introduces consis- tency or self-training signals. AT (Li al., 2022b) exploits momentum teacher, MRT (Zhao al., 2023) adds masked reconstruction, and CAT (Kennerley et al., 2024) regularizes category centroids across domains. Despite steady progress, these approaches presuppose full access to source images throughout adaptation, assumption that is often violated when data sharing is restricted by privacy or licensing.
Source-Free Domain Adaptive Object Detection (SF-DAOD) removes that dependency: only source- source-trained model and unlabeled target images are available. Since a teacher-student paradigm is required to obtain pseudo labels for supervision, current solutions mainly optimized the frame- work or the selection scheme. SFOD and SFOD-M (Li et al., 2021) select high-confidence boxes from the teacher; LODS (Li al., 2022a) and PETS (Liu al., 2023) refine labels by objectness
2

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Published as a conference paper at ICLR 2026
Pretraining
Object Queries
Suckiooe
Slzt Aware
Encoder
Queer
Decoder Head
HSA
drrean-dmmannaaa Adaptation
Baxthone
Encoder
Decoder & Mead
Fiber
Orject
W
HSA
Stot-Roare Queree
Teacher
Quenies
Decodlrr Head
Banchere
Encoder
p
CGSC
B
Slots
Student
Figure 2: Framework of CGSA. It consists of two stages: source-domain pretraining and target- domain adaptation, which work collaboratively to improve SF-DAOD performance.
or temporal smoothing; SFOD (Chu et al., 2023) and TITAN (Ashraf & Bashir, 2025) partition the target domain into siurle and Nource-dissiminn subsets according to variance, thereby recasting the fource-free setting as a traditional DAOD that can addressed with existing tech- niques. However, these methods mostly concentrate refining pseudo-labels while neglecting the object-level structural information already embedded the detector's representations. Addressing the limitation requires mechanism that can explicitly extract individual objects without access to the source data, and our work is designed to fill this gap.
Object-Centric Learning (OCL) decomposes scenes into discrete entity representations, enabling reasoning that is invariant to background shifts. Slot Attention (Locatello et al.. 2020) iteratively binds "slots" latent objects via attention. In vision tasks, slot-based models have shown strong transferability in real-world segmentation (Seitzer et al., 2022), video prediction (Zhou et al., 2022), generative modeling (Jiang et al., 2023), and even robotics (Rezazadeh er al., 2024), yet their poten- tial for domain adaptation remains largely unexplored, especially in detection, where DETR- based decoders already employ object queries but lack structural priors. Therefore, we bridge this gap by proposing novel slot-aware framework that unifies the object-centric paradigm for SF-DAOD.
3 METHOD
3.1 OVERVIEW
Under the source-free setting, object-centric disentanglement provides promising route, which separates potential targets from the surrounding scene. Once objects are isolated, object-level knowl- edge can be transferred directly, enabling implicit cross-domain adaptation without any source sam- propose CGSA, source-free object detection framework
ples. Motivated by this observation, with class- gussed supervision for fine-grained visual priors, as illustrated Figure 2. Pseudocode for the operations is provided in the Appendix E.
An input image is first processed by the backbone and query encoder to obtain object queries. In parallel, the same image passes through Hierarchical Slot Awareness (HSA), which decomposes into a coarse-to-f set of n² slots. After projection, these slots fuse with the queries to form slot-aware queries carrying object-level visual prior into the decoder.

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Published as a conference paper at ICLR 2026
(a) Hierarchical Slot Awareness
(b) Luss-Guided Slot Contrast
Feature
Crer
9999
-
Object Queriee Prototype
Stot Atternion
Object Queries
slots z(l)
- Decodler
Normalize
Reconstruction Features BO3
$
Slot Attention
Slot Attention
Hungerian Matching
Global cltas Prototype
Weighted Slots
Select
slots (
Normalize
Object
Blss
Slots Prototype
Person
Awarr
Reconstruction
((3)
Figure 3: (a) Pipeline of the proposed HSA module. Through the hierarchical design, the features first decomposed into coarse-to-fine slots. After projection, these slots are concatenated with the object queries to form slot-aware queries, thereby providing object-level structural priors. (b) Pipeline the proposed CGSC module. By maintaining the global class prototype and assigning pseudo labels to weighted slots for class attributes, contrastive learning is used to implicitly supervise and guide the slots focus on innain-invariant yet class-relevant object features.
Source-domain pretraining. The detector is trained with standard detection supervision while HSA adds a reconstruction objective, and together they provide strong structural priors for subse- quent adaptation.
Target-domain adaptation. A source-domain pretrained model. The student repeats the HSA path target images, and the result- ing attention mask weights the queries to produce weighted slots that are contrasted with dynamic class prototypes by the Class-Guided Slot Contrast (CGSC) module. The teacher generates pre- dictions on the same image, selecting reliable pseudo labels for the student based on a threshold. The weights teacher are updated by the exponential moving average (EMA) of the student.
configuration adopted, both initialized from the
3.2 HIERARCHICAL SLOT AWARENESS
We introduce a Hierarchical Slot Awareness (HSA) module, shown in Figure 3(a), that brings OCL priors into DETR-based detectors. HSA structurally disentangles an image into object-level slots in coarse-to-fine manner and embeds these regional visual prior representations into the object queries. In this way, we realize object-level cross-domain adaptation a principled and stable fashion. Ablation studies and visualization results provided in the Appendix G.
3.2.1 DECOMPOSING VISUAL PRIORS
Grounded in evidence that human vision automatically decomposes sensory input into a small set of parts under strong internal visual priors (Biederman, 1987), therefore cast visual prior decompo- sition as iterative inference and refinement process. Slot Attention (Locatello et al., 2020) meets the conditions, whose core idea to iteratively aggregate information from the input features into set of slots. In each iteration, the k-th slot computes an attention weight with an input feature h; updates itself, and after several iterations (typically 3-5) each slot 2 becomes a latent representation of an individual object. The process is expressed as follows:
(2 W (h.WT)
((+1) GRU Σ 2k
softmax,
he
(0) 2 ) (I,...,,,, E (l...., n).
Vd
(1)
where h2 E IR. 1xd is the i-th input feature, 2 E R denotes the k-thh slot state at iteration t, Wy. Wt. and W, Rddd are shared linear projection matrices. For brevity, omit the image index

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Published as a conference paper at ICLR 2026
in the following notation. N denotes the number of token features per image, and denotes the corresponding number of slots.
Given that decomposing process is unsupervised. to bind these slots to spatial support and regularize discovery, we decode slots with a lightweight spatial-broadcart broadcast MLP (Seitzer et al., 2022), obtaining per-slot reconstructions hig E R-Nxd xd and mask logits ag E RNx1, Competition among regions is enforced by softmax across slots, my (o0)) and the optimized way is to reconstruct, defined as below.
h hy @ ma
Lre = 11 h/1
3.2.2 HIERARCHICAL ARCHITECTURE
Traditional slot-based decomposition real-world datasets limits the number of slots (typically < 10) to ensure convergence and avoid collapse (Seitzer et al., 2022). break this constraint while retaining stability, we take inspiration from evidence that human vision performs progressive decomposition of scenes into parts and relations (Schyns & Oliva, 1994), first establishing coarse structural descriptions and then refining detail. Therefore, adopt a hicrarchical design in which a first-stage decoupling extracts coarse, region-level visual priors and a second-stage decoupling refines them into finer-grained priors. Concretely, first obtain coarse- grrsse-graiedd priors, denoted (1) E Raxd and h(1) E RNxd as described in Section 3.2.1. We then treat the latter as the raw features for the next level, apply the same refinement rule as in Eq 1, and concatenate the resulting representations to obtain =2) E R=nxd
A mirrored decoder predicts {he ag (2)
with masks m (2) softmax, (a (2),, yielding the (2) We supervise awareness formation both levels with the
reconstruction (2(2) k= k
2 mp
total objective:
Lree
C(1) + (.2) -1(800 - h [ h
(3)
In practice, set n=5, resulting in 2 fine slots, which retains training stability while provid- ing finer region representations and adequate granularity. Ablation studies and visualization results provided in the Appendix G.
3.2.3 SLOT-AWARE QUERIES
The slots encode structural cues of potential objects or regions. To infuse this information into the decoder, we first project the slot representations through linear mapper fmup so that their dimension matches that of the object queries. We then add them to the original queries Qooj RM g xdy obtain the slot-aware queries as final decoder input:
(2) Smapp (2(2)) E Mxd, Qaware Quij + g(2)
(4)
This fusion endows slot-aware queries with object-level structural priors, enabling more domain- invariant localization, while the original queries can be regarded the weight distribution of the visual priors.
3.3 CLASS-GUIDES SLOT CONTRAST
Existing slot-aware mechanism receives explicit semantic constraints, some slot vectors zp tend to absorb memainn-ppecffcc background noise. To steer the slots toward more yet class-relevant object features, introduce Class-Guided Slot Contrast (CGSC), shown in Figure 3(b). CGSC maintains class prototypes online and imposes a contrastive loss, thus providing implicit supervision to guide the slots towards their potential class attributes.
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3.3.1 CLASS-PROTOTYPE MEMORY
Given the M decoder queries {4: (ii and their predicted classes é {l...., Cj, we compute lemporary object queries prototype Pe for each class by averaging queries of the same class.
1
Pe
Σ q.ER.
% 2. { {g
= c).
(5)
The global-class prototype memory Pe is then updated with an exponential moving average (EMA):
P. P Pe. E 10.
(6)
3.3.2 WEIGIITED SLOT CONSTRUCTION
With denoting the original features and m (2) the weighted slot žい is defined as:
from Section 3.2.2 as the normalized attention mask,
mi ht.
Because m measures the visual evidence assigned to each feature, this aggregation suppresses slots dominated by background regions and keeps & same representation space as the decoder queries, generating reliable measure of similarity to class prototypes.
(2)
3.3.3 PROTOTYPE-SL MATCHING AND CONTRASTING
We first measure the cosine similarity between each weighted slot Σς and every decoder query qi to form the matrix Ski o( ( 9:)- Applying the Hungarian algorithm (Kuhn, 1955) yields to-one assignment π (k) that pairs slot & with query 9m(k)- Because each query carries predicted class label (+))). this assignment implicitly labels the slot č₁ (T()).
Next, all weighted slots with the same label are averaged to obtain the slots prototype žo:
Ec
kks
Ze { c3 c].
Z
Finally, we compute a contrastive loss (Oord et al., 2018: Chen et al., 2020) between the slot prototypes &. and the global-class prototypes P. as follows:
1
log exp (c))(()
Lcon
Σ
(Po,,
where 7 is temperature hyperparameter and ((:))) denotes cosine similarity. This loss pulls each weighted slot toward its matched class prototype while pushing it away from prototypes of other classes, driving the slots to capture class-specific, Nomain-invarian semantics.
Through the combined forces of class guidance and contrastive regulation, CGSC enables HSA to disentangle reliable object representations without access to source images. Consequently, the detector achieves grained, class-relevant, and slot-aware adaptation the target domain.
3.4 TOTAL ADAPTATION OBJECTIVE
Given the filtered pseudo labels )1, {(b 1 T(s)). where PÚ is the teacher's confidence and r(s) is the threshold (details Appendix C), the student's unsupervised detection objective consists of classification term and box-regression term, where use Focal Loss (Lin et al., 2017) for classification and a combination & and GloU loss (Rezatofighi et al., 2019) for box regression.
Cunsup
1
(x)) (,)) + Bood))ou(x1))))) (b).)))Es
Σ
(10)

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Published as a conference paper at ICLR 2026
Then the complete loss that drives the student network combines the unsupervised detection loss with the reconstruction loss Lie and the contrastive loss Leon. Arec free.
Lwiil
Lunsup
+ Agaa foom
(11)
3.5 GENERALIZZTION ANALYSIS
We aim to theoretically characterize how our components interact with DETR-based detector en- able reliable source-free adaptation on the target domain. In brief, our modules (i) contract domain- specific background variance, (ii) enlarge cosine inter-class margins, and (iii) propagate the margin gain through the decoder. Under standard regularity conditions detailed in the Appendix A, these effects combine into a risk descent bound for the target objective, with full statements and proofs deferred to the Appendix A.
Theorem: Risk descent on the target domain.
Let Rr (0) be the target objective in Eqs. 10-11. For the iterate 0, produced by minimizing the target objective (0), there exist constants C1, C2 > such that
E (Rx+0111
T. ((,))
c1 A, c2 (Cre +
(12)
where A, is the assine-margin gain induced by prototype-she slot InfoNCE (Eq. 9) and propagated by slot-aware query fusion (Eq. 4), while Erec and o² stem from reconstruction consistency (Eq. 3) and bounded background.
The margin term guarantees monotone decrease in the classification component. The residual is controlled by reconstruction quality and target background noise. Under a mild contraction condi- tion, the induced one-dimensional iterate converges, and the expected target risk decreases mono- tonically.
EXPERIMENTS
Table Adaptation from Small-Scale to Large-Scale Dataset: Cityscapes - BDD100K. "SF" refers to the source-free setting. "FRCNN" denotes variants based on the Faster R-CNN family; "DETR" denotes variants based on the DETR family. "Oracle" denotes an upper bound obtained by training on the target domain with ground-trule annotations.
Method
SF
Rider
Car
Truck
Bus
Meycle
Bicycle
mAP
DETR
68.2
53.2
67.9
54.1
54.7
64.3
DA Faster(Chen et al., 2018)
X
FRONN
28.9
27.4
44.2
19.1
18.0
14.2
22.4
24.9
SFA(Wang 2021)
DETR
40.2
27.6
57.5
19.1
23.4
19.2
28.9
AQT(Huang et al., 2022)
X
DETR
38.2
33.0
58.4
17.3
18.4
16.9
23.5
29.4
MRT(Zhao 2023)
30.9
22.6
DATR(Chen et al., 2025)
X
DETR
58.5
42.8
73.4
26.9
39.9
24.2
37.3
43.3
SFODLLi et 2021)
FRCNN
31,0
32.4
48.8
20.4
21.3
15.0
24.3
27.6
SFOD-M(Li et al., 2021)
FRCNN
32.4
32.6
50.4
20.6
23.4
18.9
25.0
29.0
PETS:Liu et 2023)
FRUNN
A ASsFODiChu el al., 2023)
FRCNN
33.2
36.3
50.2
26.6
24.4
22.5
28.2
31.6
TITAN[Ashrff Bashir, 2025)
DRTR
49.9
35.6
65.7
24.6
35.9
31.5
29.2
38.3
CGSA(Ours)
DETR
60.0
47.6
75.0
53.1
43.4
$3.0
4.1 DATASET
We conducted experiments on five widely used object detection datasets evaluate the performance of our proposed method. (1) Cityscapes (Cordts et al., 2016) provides 2,975 training images and 500 validation images of high-quality urban street scenes. (2) Foggy-Cityscapes (Sakaridis al., 2018) extends Cityscapes by simulating fog in the same set of images, maintaining identical anno- tations to assess robustness under degraded visibility. (3) BDD100K (Yu al., 2020) is a diverse, large-scale dataset with 100,000 driving images; in our experiments, we used the daytime subset containing 36,728 training and 5,258 validation images. (4) Sim10K Johhsonn-Rbbenee 2016) offers 10,000 synthetic images, providing large-scale synthetic-to-red adaptation bench- mark. (5) KITTI (Geiger et al., 2013) includes 7,481 real-world driving images captured from an onboard camera in urban environments.
et al.,

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+ Cityscapes. This experiment evaluates synthetic-to-real domain adaptation Published as a conference paper at ICLR 2026
4.2 IMPLEMENTATION DETAILS
For source-domain training, we employ the official pretrained weights and follow the default opti- mizer and learning rate settings (Zhao al., 2024). The HSA module pretrained the COCO dataset and further updated jointly with the network, with Aree 1.
For target-domain adaptation, we adopt the teacher-student paradigm with both the HSA and the CGSC module, with Arec and Acon = 0.05. During evaluation, we report performance using the mAP@ 50 metric. We use RT-DETR (Zhao al., 2024) our base detector. All experiments in both the source pretraining and target adaptation stages are conducted on 4 NVIDIA A100 GPUs. More details are provided the Appendix B.
Table 2: Adaptation from Normal to Foggy Weather: Cityscapes - Foggy Seesy--itsccpes...
Method
Base
Person
Rider
Car
Truck
Bus
Trala
Meyele
Bleycle
MAP
Oracle
DETR
55.6
55.8
75.2
44.8
63.5
56.1
45.6
52.2
56.1
Cheeesser(Cheee et al. 2018)
FRCNN
29.2
4014
43.4
19.7
38.3
28.5
23.7
32.7
32.0
SKA Wang 2021)
DETR
46.5
48.5
62.6
46.2
28.3
(QT(Hunn 2022)
DETR
27.7
36.0
ATILi et al, 2022))
FRCNN
45.7
54.1
62.3
31.9
54.4
49.3
35.2
47.9
47.4
MKL[Zhio et 2023)
DETR
52.8
51.7
68.7
35.9
58.1
54,5
41.0
47.1
51.2
CATiKamnaley 2014)
FRCNN
DATR(Chen al., 2025)
DETR
61.6
SFODILá 2021)
FRCNN
SFOD-M;Li et., 2021)
FRCNN
25.5
48.5
40.7
33.2
22.2
28.4
34.1
39.0
33.5
LODS:Li 2022a)
FRCNN
34.0
45.7
48.8
27.3
39.7
19.6
33.2
37.8
35.8
APSPODICbu 2023)
IRGVVS et 2023)
FRCNN
37.4
15.2
51.9
26,4
39.6
25.2
31.5
11.5
37.1
PETSLLu 2023)
PRUNN
46.1
52.8
63.4
21.8
46.7
25.5
37.4
48.4
40.3
LPLDX Yoon 2024)
FRCNN
39.7
56.6
24.6
46.3
SF-UTil al., 2024)
FRCNN
ITIANIAshma & Bashir, 2025)
DETR
$2.8
31.7
68.0
45.2
65.5
41.8
46.0
48.7
52.1
CGSAIOurs)
DETR
89.5
53.1
68.6
43.7
62.1
54.3
44.5
49.3
53.2
4.3
MAIN RESULTS
Cityscapes + BDD100K. We evaluate our method under the challenging domain adaptation scenario from small-scale, homogeneous dataset (Cityscapes) large-scale, diverse dataset (BDDIO0K), as presented in Table 1. Our approach outperforms the state-of-the-an (SOTA) source- free methods by nearly 15%, and also surpasses leading traditional DAOD methods by around 10%. This significant improvement demonstrates the strong generalization capability of our framework, particularly when transferring from limited large-scale data. These advantages indicate the poten- tial for future real-world deployment in more complex and scalable domain adaptation tasks.
Cityscapes + Foggy- Setgyy-Ctyscapes. We evalu- ate our method under the classic adaptation sce- nario from clear weather (Cityscapes) to foggy conditions (rggyyCCiysccppss. presented in Table 2. Our approach outperforms all existing SF-DAOD methods and surpasses the majority of traditional DAOD methods. These results demonstrate our modules effectively disentan- gle object-level features in the target domain and guide them toward category- consistent mantics, leading to more robust adaptation un- der weather degradation.
Table 3: Adaptation from Synthetic to Real Im- ages: Sim10K Cityscapes, Adaptation cross-camera: KITTI + Cityscapes. The results are reported using the "car" class.
Method
Oricle
DETR
Faston Chen al. 2018)
FRCNN
41.9
418
et
DAIRiChen et al., 2025)
DEIR
64.3
53.3
S 2021)
SFOD-M(Li et 2021)
FRCNN
42.9
44.6
IRG/VS et al. 2023)
FRCNN
45.2
46.9
AFSPODXClu
2023)
FRCNN
Sim10K
PETSiLinet 2023)
FRCNN
57.8
47.0
PM DCYoon al. 2034)
FRCNN
49.4
51.3
A. & Buchir, 2025)
UT
ERCNN
DETR
39.8
53.2
from Sim10K to Cityscapes, focusing the car category. As shown in Table 3, our method achieves the best performance among all ap- proaches two settings. The results demon- strate that in single-class adaptation sce-
CGSA(Ours)
DETR
67.7
60.8
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narios (see Appendix D for implementation details), model can achieve state-of-the-se perfor- mance.
KITTI- + Cityscapes. This experiment evaluates from KITTI to Cityscapes, focusing the
category across real-world datasets with different viewpoints and camera setups. As shown in Ta- ble method also achieves the best performance among all approaches in two settings. For the single-class setting, the implementation details of Section 3.3 are provided in the Appendix D.
4.4 ABLATION STUDY
4.4.1 ABLATION COMPONENTS.
To evaluate the contribution of each component in framework, conduct ablation study over all datasets, as shown in Figure 4. The results show that incorporating HSA yields consis- tent improvements across all datasets, demonstrating that structural, slot-based visual priors play crucial role in stabilizing target-domain localization. Building on this, CGSC consistently brings additional gains by aligning slot representations with class semantics in a Nommann-nvvrraan manner. The improvements are stable across diverse domain shifts, which suggests that both components are complementary, producing stronger source-free adaptation overall. See the Appendix F& for additional ablation experiments.
4.4.2 ABLATION ON THE HYPERPRRAMEEER OF HSA.
shown in Table 4, when we use two-level HSA (depth=2), the performance is not highly sensitive to the slot number. Across a wide range of numbber stays within a relatively small and acceptable band, showing robustness to this hyperparame- ter. The observed trend is both theoretically and intuitively justified, exhibiting pattern that is "low at both ends, high in the middle", where very small number (too few slots, c.g., total slots) cannot capture sufficient object structure, while very large numbers(too many slots, e.g., 64 and 100 total slots) dilutes object-centric grouping and tends to fragment objects, thus re-
Table 4: Hyperparameter Sensitivity Analysis. "Depth" denotes the hierarchy depth HSA, and "Number" is the slot count per level. The total number fine slots is Depth
{2, 4, 5, 6, 8, 10), mAP
Depth
Number
Total Slot Numbers
mAP
2
16
$2.9
2
36
52.6
100
51.2
8
50.8
64 48.7
ducing the usefulness of the structural prior.
Increasing the hierarchy depth to leads to a clear performance drop. We attribute this to the unsupervised nature of slot attention, since HSA is trained only with self-supervision, and deeper hierarchies weaken the reconstruction constraint at each level and accumulate decomposition errors, resulting in lower-quality slots for adaptation. Finally, when Depth 1, HSA degenerates standard Slot Attention. As discussed in Appendix G, training becomes unstable and often collapses when the number is large, which motivates hierarchical design. Therefore, considering both robustness and performance, we choose Depth = and Number=5 as our default setting, which gives the highest mAP of 53.2.
4.5 ANALYSIS
4.5.1
VISUALIZATION OF QUERY
Figure compares the target-domain feature distributions of the "Source Only" baseline and our ap- proach. Unlike the scattered and overlapping features in the baseline, our approach produces clearer inter-class separation and tighter intra-class clustering. This suggests that HSA provides structured visual priors that stabilize feature formation, while CGSC pulls slot representations toward class prototypes, improving semantic alignment in the target domain. The resulting geometry is consis- tent with higher mAP and supports the effectiveness of the proposed components.

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70
SOurce only
67-2
H54
.0.2
$1.8
55
13.0
50
46
16.
C2FC
C1600
S2C
KIC
Dataset
Source Only
Our Approach
Figure 4: Ablation four cross-domain benchmarks. "Source Only" denotes the baseline without adaptation, where the source-trained model is directly tested on the target domain.
Figure 5: The t-SNE visualization comparing feature
distributions of object queries on the Foggy-City Sety--Cstsssapes dataset between "Source Only" and our approach.
person
bicycle
rider
car
bus
Source Only
TITAN
Our Approach
Ground Truth
Figure 6: Qualitative comparison of our method with the "Source Only" and TITAN. The red box enlarged to highlight and compare the detection results more clearly. Our method captures fine- grained details more effectively, detecting distant objects that the other methods miss.
4.5.2 QUALITATIVE RESULTS
The "Source Only" Figure provides a visual comparison of the results on the identifies the person the left, model detects just the nearby, larger objects. Although TITAN misses some distant objects on the right. In contrast, our approach CGSA performs better, success- and bus obscured by dense fog and yielding more accurate bounding
fully detecting the distant boxes for the objects in the foreground. Notably, since the official TITAN implementation is not publicly available, we re-implemented the method and report results from our reproduction.
LIMITATIONS
Our approach tailored to DETR-based detectors because OCL and DETR share the same attention- based computation, making their underlying logic consistent. Portability two-stage non-query architectures remains to established. the transfer setting, we explore only object detection. More vision tasks, such as classification and segmentation, should be covered in future work.
CONCLUSION
We introduce CGSA, the first SF-DAOD framework that integrates Object-Centric Learning into DETR-base detector by decomposing target images into hierarchically refined slot-aware priors, and guiding them with semantics via class-guided contrast. Extensive experiments and in-depth analysis demonstrate the effectiveness and robustness of the proposed components and the gener- ality the framework. The object-centric paradigm also opens a promising path for the privacy- preserving domain transfer.
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ACKNOWLEDGMENTS
This work supported in part by Hong Kong Research Grants Council under NSFC/RGC Collabo- rative Research Scheme (Grant CRS HKU703/24) and the focused project on spatial intelligence at Shenzhen Loop Area Institute.
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APPENDIX
A Proof
15
Experimental Details
18
Dynamic threshold selection
18
D Single-Class Protocol on Class-Guided Slot Contrast
19
Pseudocode
19
Ablation: Dynamic Threshold Schedules
21
G Ablation: Slot Number and Architecture
22
H Visualization of the slot-based decomposition
23
Empirical validation of theoretical analysis
25
J Impact of COCO pretraining for HSA
25
K Computational overhead
26
Ethics statement
27
M Reproducibility statement
27
The Use of Large Language Models (LLMs)
27
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A
PROOF
Why Guided Slots Form Domain-Invariant Priors and Yield Convergent Improvements in Source- Free Target Adaptation?
A.1 STANDING SETTING AND NOTATION.
We consider a C'-class detection task with image space X' C # and label space y of finite [0, 1] and fl....,C' C).. The source and target joint distributions factorize as Pxy PS FIx and P P
sets of pairs (b, c) with
The learning protocol contains two phases. On the source pretraining, a DETR-based detector fo is trained on Dg by the standard detection loss, yielding 0g the target adaptation, only Dr PX is available; maintain student- pair (0.. 02) initialized by 0 and update the teacher by EMA 0g - 7 0g (1 >) 0.. Teacher predictions turned into training targets via (()))); and the student minimizes Bx-pz [Faet (f,, (x), (()x)))))
We follow all the modules, losses, and notation as defined in the main text: hierarchical slot con- struction and reconstruction losses (Eqs. 1-3), slot-aware query fusion (Eq. 4), class prototype main- tenance (Eqs. 5-6), weighted slots, matching, and contrastive training (Eqs. 7-9), and the target- domain objective (Eqs. 10-11). The hypotheses H1-H3 below assumed in all proofs.
A.2 HYPOTHESES
H1 Bounded background: the backbone feature can be decomposed as h 1bobj + bg with EE X| = 0 and CRv(h = TT
H2 Reconstruction consistency: the hierarchical reconstruction objective in Eq. 3 in- duces a slot projection S(h) that is approximately identity on the object subspace S, i.e., [k) [Is(hbbi)]|* S Eree.
H3 Prototype semantic stability: the class anchors Pa maintained by Eqs. 5& have bounded drift across iterations, and enjoy a positive cosine margin w.r.t. same-class weighted slots.
A.3 LEMMA (VARIANCE CONTRACTION: WEIGHTED SLOTS SUPPRESS DOMAIN-SPECIHI BACKGROUND)
Claim. Let the weighted slot &n be defined as in Eq. 7. Under H1 & H2,
E ق
kob) |? I 6xec + Ky 0².
m (2)
12 (0,
(A.1)
Here hobj denotes the effective component h in the object subspace S. The quantity Ki = [|mm]] 11 acts clear contraction factor.
Proof. Write hobi bbg as in H1. Then by the convex-variance bound,
Var (m (2) bB)
m
12
02
Nig o².
(A.2)
By the reconstruction consistency in H2, the slot projection S(h) is approximately identity S, i.e. E ((h)))) - (hi))]) < érec- Identifying 5(h) with the weighted aggregation above yields
equation A. This completes the proof.
A.4 LEMMA (INFONCE ENLARGES COSINE INTER-CLASS MARGINS SUPPRESSES CROSS-CLASS SIMILARITY)
Claim. Let the contrastive loss between class prototypes and slot prototypes be Eq. 9, i.e., an InfoNCE/NT-Xent objective with positive pairs (Pr, =e) and negatives (Pe, E.). Then the gradients w.r.t. the similarities satisfy
OCron
Tpos
OLoon
Tneg
0,
> 0,
(A.3)
Ús pas
7
Is Dnng
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where softmax weights and is the temperature. Consequently, gradient-descent step Cen increases the same-class similarity and decreases the cross- similarities, thereby enlarging the cosine margin
A. (Par =
m -
=c).
(A.4)
c'
Proof. Write the InfoNCE loss in Eq. 9 for one anchor Pa as
exp( /T)
Lcon - log
expl + De'fo exp(s (8c
(A.5)
Spos + log p)pppp/////)) + Σ exp(x>
noR
/T))
where Spo= := ==p(P
((Pc,2 2c) and se' == cp(Po 2cr) Let
exp(Spos/ TT)
exp(s" nog /T)
Z exp($pos/T)
Σ exp(s>"*/ /T),
Tpos
Tee
7
e'
(A.6)
Then
eonn
1 + Z exp(spas/7)
1 1
1 Tpos
< 0,
(A.7)
as pos
and for any negative d' + c,
OLenn
1
(xpps50x00 /T)
Tel 0.
(A.8)
as neg
Z
Therefore, under a gradient-descent update s f- TT /0s with step size 7) > 0, we have Spos strictly increases and each s² strictly decreases. Since the cosine margin A, is the difference between the expected same-class similarity and the maximal cross-class similarity, these monotone updates enlarge A, (in expectation over samples). The construction of (Pe, Re) via Eqs. 5- 8 ensures semantic stability of positives and well-defined negatives across steps. The gradient form above is the standard derivative of the log-softmax used in CPC/SimCLR analyses (Oord al., 2018; Chen et al., 2020). This completes the proof.
A.5
LEMMA 3 (MARGIN TRANSFER: SLOT-AWARE QUERY INFUSION YIELDS AT LEAST LINEAR DECODER MARODER-MMRRGN GAIN)
Claim. Let the decoder y be locally L-Lippchitz in its query input, and let Qaware Qobj 2 as in Eq. 4. We use the shorthand Ž := z(2) for the stack of mapped level-2 slots. Assume moreover that on the subspace S = span ( Pet the map Q + g(Q) is locally bi- bi-Lisschitz with constants (J,, L). Then
margin (Qaware) margin (Qonh)))
1 [|
(A.9)
where || denotes the norm of the component of Ž aligned with the span of [Pc].
Proof. Let o(Q) == margin (Q) denote the decoder-level margin, i.c., o(Q) = Spos
max. ueg (Q), where each similarity score sQ) (PP, g(Q)) is composed with g. Write II for the orthogonal projector onto the prototype-aligned subspace S = span (Pc) and decompose Ž = =Zeeff + 21 with ZeH == 11Ž. Since is locally L-Lipschitz, every score s(Q) is locally L-Lipschitz in Q, and so ((Q) up the 1-Lipschitz max operator. Assume moreover that on the subspace S the map Q + (()) is locally bi-Lipschitz with constants (p, L), i.e., for all small A S, | < lg(Q + A) g(Q)]l S L|| (When the Jacobian Jq(Q) restricted S is nonsingular, such μ > 0 exists by the inverse function theorem. See, e.g., (Rudin, 1976).)
By the mean value theorem for vector-valued functions (Rudin, 1976).
Φ(Qob) + -) -(Q0b))) ( + +tū), 2) dt.
(A.10)
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For lower bound, it suffices to consider the projection of Z onto S; hence work with Zen == IIZ and drop the orthogonal component:
[o(b + 2) - ((Qbi)) 2 [VVolQui)+tt Zea) dt
2
inf [E
Vo(Qob; + +tāi 2
(A.11)
Along S, variations of & are controlled from below by the bi-Lipschitz constant of g: in partieular, obtain
(Qob) + Z) ((Qoi)) 2 # -l
(A.12)
Recalling that || [| yields the claim. This completes the proof.
A.6 THEOREM (A BASIC RISK DESCENT INEQUALITY ON TARGET DOMAIN)
Let the target risk (0) regularization Eq. 11. Under H1-H3 and per-step minimization, there exist constants C₁ 02 such that
(0) be the expectation of the target-domain detection objective in Eq. 10 plus
E ERy (0s+1) < IE (O)) - e1 A, + 02 (crec + o²).
(A.13)
Proof. We decompose the one-step change of the expected risk into (i) margin- driven decrease in the classification component and (ii) a residual term due to representation noise.
(i Margin gain. By Lemma 2, the InfoNCE objective in Eq. 9 yields monotone updates that increase the same-class similarity and decrease cross-class similarities, enlarging the cosine margin A, on the slot-prototype pairs. By Lemma 3, this margin gain transfers through the decoder at least linearly. Hence there exists a₁ > 0 such that the classification part of the risk satisfies
E (0--1) & E (0) - a1 A.
(A.14)
(ii) Residual noise. By Lemma 1, the weighted-slot aggregation contracts the background variance, and the reconstruction consistency H2 bounds object-subspace approximation error by frec. Con- sequently, the representation noise contributes at most a term d2 (érer + o²) to the change of the total risk, for some d2 >> that absorbs Lipschitz constants of the detection loss and the decoder.
Combining (i) and (ii), and noting that Eqs. 10& 11 just add Lipschitz-contintous regularization terms, we arrive at
E (T(0++1) I (T))))) 01 A, 02 (Erec + o²).
(A.15)
Renaming ei = ai and 02 == d2 gives equation A.13. This completes the proof.
A.7 COROLLARY (CONVERGENCE VIA CONTRACTION)
Notation. Let π) E [0, 1] denote effective noise level (e.g., upper-bouse bound proxy of classifica- tion/matching error on the target domain) at step t, and let A 2 0 be the margin increment defined above. Consider the iteration
77 + M = 7he A, + 3 (Croc + +²)).
(A.16)
where 0 summarizes the efficiency of contrastive/proootyee alignment and query infusion, and 3 2 O captures residual effects of reconstruction error and background variance. By Lemmas 2-3, there exists & such that A, (m n for some fixed point 17".
Claim. If O < ak and ((ree + 0²) $ - ak) r7,, then + is a contraction on [o, 1]:
(()) ( < ak) In -n'l (Vn, e [0, 1]).
(A.17)
Hence, by Banach's fixed-point theorem (Banach, 1922), ?t converges to the unique fixed point 17" Together with the risk inequality equation A.13 and A k(m 1)*)., E (R,())) decreases monotonically and converges.
Proof. By definition, (#()) Φ(rr)1 a (n))) A(7') < ak i r/l- Let p ak < 1; contraction follows. Banach's fixed-point theorem then yields existence/uniqueness and geometric convergence. Summing equation A.13 over yields risk convergence. This completes the proof.
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B EXPERIMENTAL DETAILS
B.1 SOURCE-DOMAIN PRETRAINING
During the source pretraining stage, we train the detector only on source-domain images. The training is based on RT-DETR (Zhao et al., 2024) with official pretrained weights on COCO dataset and its default optimizer and learning rate configurations. The HSA module, pretrained for 100 epochs on the COCO dataset, is included in the training process, and the reconstruction loss is applied with a weight of Xree = Training is conducted for 72 epochs with gradient clipping at maximum norm of 0.1. Mixed precision and exponential moving average are enabled, where EMA configured with a decay 0.9999 and a warmup 2000 steps.
The optimizer is AdamW. Backbone layers excluding normalization are trained with learning rate of le-5, encoder and decoder normalization layers are set with zero weight decay, and the remaining parameters base learning rate of lc-4 with (0.9, 0.999) and weight decay of lc-4. The HSA module optimized with separate learning rate of 1e-5. Learning rate scheduling follows a MultiStepL.R strategy with milestones at 1000 iterations and decay factor of 0.1, together with linear warmup scheduler of 2000 steps.
B.2 TARGET-DOMANI ADAPTATION
During the target adaptation stage, we adopt teacher-student architecture and run 12 epochs. The teacher updated by an exponential moving average with decay 0.9993, initialized from the source- pretrained student.
The training objective in the CGSA component adds contrastive term with coefficient Acon 0.05. All other settings are identical to the source stage, including the use of HSA (with its reconstruction loss weight Area
and its learning rate of 10-5), the RT-DETR defaults, optimizer and parameter groups (AdamW with the same learning rates, betas, and weight decay), learning-rate scheduling and warmup, mixed precision, gradient clipping, and any remaining implementation details. All experiments in both the source pretraining and target adaptation stages conducted on NVIDIA A100 GPUs.
During inference, the HSA remains active while CGSC is turned off, so the parameter count in- creases only marginally and the model does not become overly large.
C DYNAMIC THRESHOLD SELECTION
During target-domain adaptation, following previous work, employ standard teacher-student framework. At each step, the teacher network generates pseudo labels ÿ) for mini-batch of tar- get images X;; the student network is trained with these labels and updates its parameters 0, via backpropagation, while the teacher parameters 0, are updated with an exponential moving average (EMA):
0 + 70 + (1 - 7) 0,.7 E [0, 1).
(C.1)
Because supervision entirely relies on pseudo labels, selecting reliable ones is critical to stable adaptation. Rather than fixing a single confidence threshold, we propose a cosine-adaptive strategy that gradually relaxes the threshold as the student improves. Denote the current optimization step by s and the total number of adaptation steps by The threshold r(s) [Tminy Tmax] computed as:
1 + cos(IT s/S)
T T(s) Tiin + (Tmax Tmin)
(C.2)
This schedule starts with strict filter (r 8 Tmax) to suppress early noise, then gradually lowers the bar, allowing more pseudo labels to participate once the detector becomes more accurate. The smooth, mo-moootonic decay avoids abrupt changes that could destabilize training and requires no hand-tuned milestones. We also construct parameter ablation and comparative experiments, see Appendix F.
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D SINGLE-CLASS PROTOCOL ON CLASS-GUIDED SLOT CONTRAST
In the datasets SIMIOK -CCTYSSPESSS and KITTI-CITYSCAPES, detection is conducted under single-class protocol focusing exclusively on the car category. Concretely, SIMIOK provides bounding boxes only for cars, while CITYSCAPES contains multiple traffic-related categories but evaluation restricted cars. Similarly, when transferring from KITTI CITYSCAPES, only the car annotations and treat all non-car instances as background. This setting isolates domain shift without confounding class imbalance or multi-class interference, and it aligns with prevalent practice in the DA detection literature.
retain
Under this single-class setting, our slot slot- query matching and prototype construction simplify accord- ingly. Let & be the k-th weighted slot and the i-th decoder query. We compute cosine similarities
Sk = 0(2k: 9c),
(D.1)
and obtain one-to-one assignment (k) via the Hungarian algorithm applied to S. Since every valid detection hypothesis is of the same semantic class (car), the assignment induces a trivial label for each slot, i.e.,
C for all k
(D.2)
so that all slots fall into a single index set
Zeaz
{
0x car) } 21, K}.
(D.3)
We then form the single-class slot prototype by averaging all weighted slots:
Zear
2k.
(D.4)
1ceer
Be Zear
The contrastive regulation in the multi-class case, where each class-specific slot prototype is pulled toward its corresponding global prototype and pushed away from the remaining prototypes, reduces here to a pure alignment objective, because the InfoNCE denominator collapses when C = Let Pooz denote the global prototype of the car class. The single-class contrastive loss becomes
Csingle =- -((oorr Zcar),
(D.5)
which maximizes the cosine agreement between the dataset-level car prototype and the slot-level prototype. Intuitively, this objective preserves the attractive force that consolidates car-specific semantics across domains while discarding inter-class repulsion that is meaningless in a single- class regime, thereby stabilizing representation learning and ensuring that the slots encode domain- invariant, class-relevant information for cars. In implementation, we set C'=1 so that the label indices become degenerate. We compute single dataset prototype of dimension D, and replace the multi-class InfoNCE with the alignment objective above. All other components, including slot weighting, similarity-based matching, and exponential moving average updates of the dataset proto- type, remain unchanged and operate identically under the single-class constraint.
E PSEUDOCODE
Below is the pseudocode from Section 3.2.1& 3.2.2 for reference.
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Algorithm Hierarchical Slot-Based Reconstruction
Require: Input features h, token length N, per token feature dimension number of slots n, slot
dimension d
Stage Coarse Decomposition
D obtain coarse slots
2: 2(1) &- Encode h with slot attention
3: Broadcast z(() across tokens
4: (I(2), mn(1) k- Decode into reconstruction and masks
5: Split (())) into slot-specific feature maps
6: Stage 2: Fine Decomposition
D refine into fine slots
7: Initialize _(2) <- Gaussian distribution
8: for each coarse slot feature map do
9: Get fine slots <- Encode with slot attention
10: Append to 2(()
end for
12: Concatenate into n slots
13: (h(2), m²(2) < Broadcast (22) and decode
14: Stage 3: Weighted Slot Features
aggregate features
15: Normalize masks !²)) across tokens
w(2) <- Weighted combination of m2)) and input features h
17: Stage 4: Reconstruction Loss
free ||A(0) 1112 || h
19: Outputs: h(22, 22) (2) w(22
Below the pseudocode from Section 3.2.3 for reference.
Algorithm Slot-Aware Target Fusion with MLP
Require: Object queries tgl E Bxxxd slots Bxnxd slot mapper Smp Bx
Bxaxd
1: Compute segment length +- L/n
2: Initialize fused segments F +-
3: for i = 1 to do
4: Extract target segment (gl, +- tgt(; (i - 1)8 is,
D B x 8 x D x D BBxsxd
5: Select slot 2; 2|:, : 1,:]
6: Map slot to segment mapped_ +- Smap(
7: Fuse with target segment týt, + tgt, + mapped
8: Append tğt, to F
9: end for
10: Concatenate fused segments tgt +- Concat(F, dim 1)
11: return lgt
Below is the pseudocode from Section 3.3 for reference.
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Algorithm 3 Class-Guided Slot Contrast (CGSC)
Require: Decoder queries (0ih with predicted labels e; weighted slots {& J class proto-
types {Pr]
1: Stage 1: Class-prototype memory
2: for each class c = 1... C do
3: Collect queries 2. {a è = c]
4: if' Q. not empty then
5:
Compute temporary prototype Pc -- mcan Qc) 6: Update global prototype P. t B.. + (1 3)
7: end
8: end for
9: Stage Weighted slot construction
10: for each slot k == 1.... do
11: Aggregate features žy + Σ, mgg
(2)
h4
12: end for
13: Stage 3: Prototype-slot matching
14: Compute similarity matrix Ski f- Φ(2x, 9:)
Get one-to-one assignment (k) < Apply Hungarian algorithm
16: Assign pseudo label C +- Cx(())
Stage 4: Slot prototypes
18: for each class = 1... C do
Collect slots 2g t {1: c c}
20: if Z, not empty then
Compute slot prototype & t mcan({2x]
22:
end if
23: end for
24: Stage 5: Contrastive objective
25: Compute loss:
exp(o(Pe,
Lcon - log Σ explolPe. 2c1//)
return Lcon
F ABLATION: DYNAMIC THRESHOLD SCHEDULES
We investigate the effect of threshold scheduling on target-domain adaptation under teacher- student self- olf-ttriinng framework. After an initial burn-in in stage, different scheduling functions T(s) are used, where s E [0, S] is the step index after burn-in and is the total number of adaptation steps.
Fixed threshold
Tixed (s)
Tix-
(F.1)
Cosine (our method, see the Appendix C)
Teas (s) = Tiii + (Timax Tanin) 1 + cos( 8/S)
(F22
2
This schedule starts strict (5 RS Tiaax) and gradually decays to Tmin in a smooth and symmetric manner.
Exponential
Tesp(s) Tunin (Truxx Tuinn) exp(-
-8s,, -
(F33
where 3 > 0 controls the decay speed.
Sigmoid
Tsigrmoid(s) = Tmin + (Tmax Tmin) expl -( 11)
(F.4
where l controls the steepness of the eurve.
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Table F.1: Comparison of threshold schedules. The cosine schedule with 0.55->0.40 achieves the
best result.
Range
Method
mAP
0.55-+0.40
+0.40
Cosine
53.2
0.80->0.40
Cosine
45.7
0.55->0.20
Cosine
51.2
0.80~>0.20
Cosine
47.0
0.50
Fixed
50.9
0.40
Fixed
48.9
0.55
Fixed
50.8
0.55
+0.40
Exponential Sigmoid
50.5
0.55 +0.40
Analysis. Table F.1 summarizes the results (mAP@50) under different scheduling strategies and thresholds. The comparison shows that cosine scheduling consistently outperforms fixed thresholds. achieves 53.2 mAP, which surpasses the best
In particular, the best cosine setting (0.55-00.44 fixed threshold (0.50, 50.9) by margin of +2.3. This demonstrates the advantage of progressively relaxing the confidence threshold rather than keeping static throughout training.
The choice of threshold bounds also has clear impact. When Tmax is set too high (e.g., 0.8), pseudo-label coverage becomes overly restrictive in the early stages, leading to poor adaptation results (45. 7/470/...). Conversely, when Tmin is set too low (e.g.. 0.2), the model admits too many noisy pseudo labels in later stages, which also degrades performance (51 551.2/47.0) Moderate ranges, such 0.55-00.40, provide the most balanced trade-off between early precision and late recall.
When comparing different dynamic strategies under the same threshold range (0.55-+0.40), the cosine schedule again proves most effective (53.2), outperforming exponential (50.5) and sigmoid (48.8). The key reason that cosine decay (Eq. F.2) introduces a smooth and symmetric relaxation, which avoids the overly aggressive early drop of exponential decay (Eq. F.3) and the saturation effects that slow down sigmoid adjustment (Eq. F.4).
From a practical perspective, in noisier domains, slightly higher Tmin may be beneficial suppress noise, whereas in cleaner domains, reducing Tmin can further expand recall without sacrificing sta- bility.
G ABLATION: SLOT NUMBER AND ARCHITECTURE
To further justify choice of HSA, conduct both qualitative and quantitative ablations against vanilla Slot Attention (SA) under different slot numbers. Figure G. visualizes the per-slot masks; Table G.1 reports the detection performance (mAP@50). Unless otherwise stated, the training schedule, backbone, and detector remain fixed; only the slot mechanism differs.
SA (num_slow slots = 5). With only 5 slots, SA roughly separates foreground from background but remains too coarse: fine-grained structures (e.g., cars, pedestrians) are merged into large regions, limiting the discriminability of object-level priors. This indicates that small slot budget is insuffi- cient for complex scenes.
SA (num slots = 25). Simply increasing the slot count to 25 does not yield finer decomposition. Instead, observe severe striping artifacts, where multiple slots collapse into redundant, nearly vertical partitions, reflecting training instability under large slot numbers real-world data. No- competition across slots. Collapsed or
tably, although we set 25 slots, the masks are produced low-confidence slots receive near-zero weights and thus become effectively inactive or overlap with dominant slots. The final argmax visualization therefore does not exhibit 25 distinct segments.
HSA (num slots 5 x 5). HSA performs coarse-to-fin decomposition: five stable coarse slots first established and then refined into 25 fine slots, avoiding the collapse of vanilla SA. As shown
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in Figure G.I, HSA generates spatially coherent, object-aligned partitions that achieve both stability and granularity.
Original Image
HSA coum_slots=5'5
SA sum__lots=55)
SA (num_slots==2) slots=25)
Figure G.1: Visualization mask decomposition under different configurations. Top-left: Original image. Top-right: HSA (5 5 slots). Bottom-left: SA (5 slots). Bottom-right: SA (25 slots). HSA achieves both stability and granularity, while vanilla SA either under-segments or collapses.
Table G.I:
Quantitative ablation on slot configurations (mAP@ 50). HSA consistently outperforms vanilla SA under both small and large slot settings.
Method
mAP
SA maum_slots 5)
50.6
SA (num slots = 25)
50.2
HSA (5 5 slots)
53.2
Analysis. From Table G.1, observe the following: (i) SA-5 attains 50.6 mAP, matching the qualitative observation that coarse segmentation captures only parts of the foreground. (ii) SA-25 fails to improve due to slot collapse and redundancy-some slots become inactive and provide little additional capacity. (iii) HSA (5 5) achieves 53.2 mAP, validating that hierarchical decomposition delivers stable training and effective fine ane-grained priors.
Therefore, HSA is superior to vanilla SA: (i) it alleviates segem-mtmttttiionn with few slots; (ii) avoids collapse when the slot count is large; and (iii) provides stable, fine-grained object-level priors that benefit fource-free adaptation.
H VISUALIZATION OF THE SLOT-BASED DECOMPOSITION
Visualizations of the slots. As shown in Figure H.l, across diverse driving scenes in the BDD100K, the slots consistently partition the image into spatially coherent regions that roughly align with objects or large structures (e.g., cars, road surfaces, buildings, sky, vegetation). Impor- tantly, this behavior emerges without any pixcl-level supervision: HSA is trained purely with reconstruction objective and competitive assignment among slots. As result, the decomposition is
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Figure H.1: Additional visualizations of the slots.
not intended match semantic segmentation perfectly. This is main reason why some regions span multiple semantic categories, and some small objects may be merged with nearby background. This phenomenon is expected in unsupervised slot-based models and has also been observed in prior object-centric learning works (Locatello et al., 2020; Seitzer et al., 2022).
What HSA provides, therefore, is not semantic segmentation, but rich structural prior: the model is encouraged to represent the scene using small number of object-like regions, whose boundaries tend to follow strong edges and instance contours. This prior is exactly what our theoretical analysis exploits to reduce background variance and extract domain-invariant features. To further enhance the semantic consistency of these structural slots, we introduce the CGSC module, which aligns slot prototypes with class prototypes during adaptation. HSAfocuses how the scene is decomposed, while CGSC focuses on which class each region should correspond to.
epoch 5
epoch 20
epoch 40
epoch 60
epoch 80
epoch 100
Figure H.2: Visualization of the decomposition process
Visualization of the decomposition process. To make the slot decomposition process more in- tuitive, visualize the masks produced by HSA different training epochs the same target image from the BDD100K, as shown in Figure H.2. At the beginning of training (epoch 5), the slots appear irregular, fragmented patches that mix cars, road, and buildings, indicating that the model
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has not yet learned meaningful groups. As training proceeds (epochs 20 20-80). the decomposition becomes progressively more structured: slot boundaries increasingly follow strong image edges and large regions such road, sky, and building facades are covered by few coherent slots, while other slots start to focus on car-shaped regions. By epoch 100, the slots stabilize into consistent partition of the scene, where a few slots cover the road, several specialize on individual cars rows of cars, and others capture background structures like buildings and sky.
This evolution from noisy, mixed patches clean, object-like regions illustrates how HSA progres- sively learns an object-centric decomposition without any pixel-level supervision. The visualization supports our claim that HSA provides meaningful structural priors as the slots specialize coherent regions that roughly correspond to objects or large parts over the course of training.
I EMPIRICAL VALIDATION OF THEORETICAL ANALYSIS
Average Cosine madiin-marrgnncco Gain
Reconstruction Loss Comparison
037
ang conine-margan gain
0.011
0.20
loss rnconstructio
025
0.19
0.088
023
0.07
0.2
0015
022
0024
630
Epoch
Epoch
(a)
(b)
Figure I.1: Empirical validation of cosine-margin gain and reconstruction consistency
To further substantiate theoretical analysis. now provide training dynamics on the -itsccapes-Foggy Cityscapes. The reported cosine-margin gain is computed exactly according to the definition in (A.(A.4) of the appendix, and for the reconstruction consistency of HSA additionally test two reconstruction objectives (L1 and L2) to verify that the observed convergence behavior not tied a specific metric.
As shown in F(a, the average cosine-margin gain increases steadily during training, rising from about 0.014 at the start to above 0.022 at the end. This monotonic upward trend empirically supports our theoretical claim that CGSC enlarges the cosine margin between same-class and cross- class prototype slot similarities, which a key condition in our risk-descent analysis.
Meanwhile, Iig. shows that the reconstruction loss decreases consistently over epochs under both L1 and I.2 formulations, indicating progressively better and more stable slot-based decompo- sition, which we refer to reconstruction consistency. Importantly, both reconstruction objectíves lead to the same qualitative trend of steady convergence, suggesting that our approach is robust to the specific choice of reconstruction metric. Quantitatively, L2 reconstruction yields a slightly higher final detection performance (53.2 vs. 52.5 mAP), so we adopt L2 as the default in our framework.
J IMPACT OF COCO PRETRAINING FOR HSA
To quantify the impact of COCO self-supervised pretraining, we have conducted experiments on the Cityscapes- Foggy where HSA is initialized randomly (no COCO pretraining), with all other experimental settings identical to our approach. The results shown in Tab.J.1.
From Tab. J.1 we observe that, even without COCO pretraining, adding HSA and CGSC already brings large gains over the baseline: HSA alone improves mAP from 46.2 to 51.2 (+5.0), and the full CGSA further raises it to 52.7 (+6.5). This indicates that HSA can be learned purely from the in- domain data (source and target) and still provides strong object-centric priors for adaptation. COCO
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Table J.1: Ablation Study of COCO pretraining for HSA.
Setting
COCO pretraining for HSA mAP
Source only
46.2
w/ HSA
51.2
w/ HSA & CGSC (CGSA)
52.7
w/ HSA
51.8
w/ HSA & CGSC (CGSA)
53.2
seff-super pretraining gives an additional but moderate boost: the mAP of HSA increases from 51.2 to 51.8 (+0.6), and that of CGSA from 52.7 to 53.2 (+0.5).
In other words, only small fraction of the total improvement over the source-only baseline can be attributed to COCO pretraining, whereas the majority of the improvement stems from the proposed mechanism itself. Therefore, COCO pretraining is helpful but not critical.
1-SNE Image Features: COCO VOC
t-E image Features: COCO Cityscapes
L-SNE Image Features: COCO Foggy Cityscapes
Image
COCO
Image
COCO
COCO
SIML0K
KITI
COCO 800100K
Figure J.1: Visualization of distribution differences between COCO and all datasets used in our experiments.
Besides, to further explore how COCO pretraining affects the generalization capability of HSA, we visualize the feature distributions of COCO and all datasets used in our experiments. as shown in F.g. To ensure fair comparison, we use the same frozen DINO backbone as in HSA pretraining, we extract global image features and apply t-SNE for dimensionality reduction. As reference case, we also plot COCO Pascal VOC (first row, first column). These two classic datasets are both generic web images with centrally framed, prominent objects, and their t-SNE embeddings significantly overlap, confirming that their distributions are relatively similar.
In contrast, when we compare COCO with Cityscapes, Sregy-Cityscapes, BDD100K, Sim10K, and KITTI, the two feature distributions are clearly separated in every plot. These datasets are all related to driving scenarios but differ substantially from COCO's general, static object photographs. Never- HSA consistently improves performance across all these benchmarks
theless, our COCO-pretraine in the main experiments. This strongly suggests that the COCO- -DCO--retrained HSA generalizes well, evidenced by its ability transfer the object-centric structural prior learned from generic web images to very different driving domains, rather than overfitting to COCO-specific statistics.
K COMPUTATIONAL OVERHEAD
To analyze the computational overhead HSA, we report the parameters, per-iteration processing time, and mAP two different base detectors: very lightweight RT-DETR (Zhao al., 2024)
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Table K.1: Analysis of computational overhead. Results measured the Cityscapes Foggy-
Cityscapes setting.
Base Detector
Component
Parameters
Time
mAP
RT-DETR (Zhao et al., 2024)
43M
269ms
46.2
+ HSA
63M
343ms
51.8
A%
46.5%
27.5%
12.1%
FocalNet-DINO-DETI (Yang et al., 2022)
228M
780ms
43.8
+ HSA
251M
809ms
55.0
A%
10.1%
3.7%
25.6%
and a large FocalNet-DINO detector (Yang et al., 2022). As shown in Table K.1, we can observe that our method achieves significant performance gains both detection frameworks. Although the introduction of HSA increases parameters by 46.5% and training time by 27.5% on the lightweight RT-DETR, is essential to note that RT-DETR designed as an extremely lightweight DETR variant for real-time detection, comparable to YOLO-style models. Consequently, any non-trivial contrast, on module inevitably results in a relatively large percentage increase in parameters. the much heavier FocalNee-DINNO, the relative overhead is much smaller (10.1 % parameters, 3.72% training time), yielding a remarkable +25.6% mAP improvement. This makes the computational cost of HSA negligible. These results demonstrate that HSA introduces controllable and reasonable computational overhead.
L ETHICS STATEMENT
This work focuses on proposing and analyzing methodological advances, with all experiments con- ducted publicly available datasets and open-source models. No private sensitive data involved in this study. Our contribution lies in understanding the underlying mechanisms and im- proving model performance, rather than deploying downstream applications that may raise ethical or societal concerns. Therefore, this work does not pose direct risks of misuse.
M REPRODUCIBILITY STATEMENT
We have made significant efforts to ensure the reproducibility of our work. The main paper de- scribes the model architecture and training procedure in detail (Section 3). Additional implemen- tation details and hyperparameters are provided in the Appendix B. The pseudocode of the main algorithm presented in the Appendix E. The code is released at https: / hiihhb..b... com/ Mi chael - TMMMccueee/CSSSS
N THE USE OF LARGE LANGUAGE MODELS (LLMs)
The authors used ChatGPT-5 to solely polish the readability and grammatical accuracy of selected passages after the initial draft was completed.
27


Reviews:
[{"review_id": "XN8AGb2QwE", "reviewer": "Reviewer_WgAU", "summary": "This paper studies the problem of Source-Free Domain Adaptive Object Detection (SF-DAOD), where a detector trained on a labeled source domain is adapted to an unlabeled target domain without accessing source data during adaptation. The authors argue that existing SF-DAOD approaches neglect object-level structural information across domains. To address this issue, they propose a new framework named CGSA, which introduces Object-Centric Learning (OCL) into SF-DAOD through slot-aware adaptation. The framework contains two main components: (1) Hierarchical Slot Awareness (HSA), which disentangles image features into slot representations; and (2) Class-Guided Slot Contrast (CGSC), which enhances semantic consistency and promotes domain-invariant adaptation. Experiments on five cross-domain object detection benchmarks show that CGSA achieves consistent performance gains compared to prior SF-DAOD methods. The paper also provides some theoretical analysis to justify the generalization of the proposed approach.", "questions": "Please see the weakness.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The authors conduct experiments on multiple benchmark datasets, showing that their method achieves better performance than previous approaches.\n\nThe paper includes theoretical discussions supporting the generalization ability of the proposed method.", "weaknesses": "The paper’s description of the proposed method, especially the role of slot attention, is unclear. From the current presentation, it appears that slot attention is applied to the queries in DETR. However, it is not clear how this differs in essence from standard attention mechanisms. The authors should provide a clear comparison or ablation study to demonstrate why slot attention is necessary in this context.\n\nThe paper claims that slot attention helps capture object-level features. However, the provided visualization (Figure G.1 in the supplementary materials) does not clearly show the effectiveness of slot-based decomposition. The authors should include more convincing visual evidence or quantitative analysis to illustrate how slot representations correspond to distinct object structures.\n\nThe proposed method is built on DETR, while several compared SF-DAOD methods are based on Faster R-CNN. Since DETR generally outperforms Faster R-CNN even without adaptation, the comparison may not be fully fair. The authors are encouraged to implement their framework on Faster R-CNN to ensure a fair comparison and isolate the improvement brought by the proposed adaptation mechanism from that of the detector architecture itself.", "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": "Ee7gb3j47y", "reviewer": "Reviewer_jc6a", "summary": "This paper proposes a framework called CGSA, specifically designed for source-free domain adaptive object detection (SFA-OD). CGSA embeds object-centric slot representations into DETR queries, progressively disentangles target images into coarse-to-fine slots and aligns slot prototypes with online class prototypes. Experiments show the effectiveness of the proposed CGSA.", "questions": "Please refer to Weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "* The paper is easy to follow.\n* Using a slot-aware framework for object-level alignment is a reasonable approach.\n* Experiments on five cross-domain shows the effectiveness of the proposed method.", "weaknesses": "1. The performance of the base model should be reported(e.g., RT-DETR) for better evaluation.\n2. As shown in Figure 3, the HSA module adapts the pre-trained DINO model. Therefore, the additional computation overhead needs to be analyzed.\n3. Since the method uses a pre-trained model to inject feature knowledge, it is recommended to add some comparative introductions with existing methods that use VLM for knowledge injection, such as [1][2]. And recent DETR-based SFOD methods should also be discussed, such as [3].\n\n[1] Da-ada: Learning domain-aware adapter for domain adaptive object detection. NeurIPS 2024\n\n[2] SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection. CVPR 2025\n\n[3] Source-Free Object Detection with Detection Transformer. IEEE TIP 2025", "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": "2aLqvv41lw", "reviewer": "Reviewer_5pPM", "summary": "This paper proposes integrating object-centric learning into DETR-based detectors for source-free domain adaptive object detection. Specifically, the Hierarchical Slot Awareness (HAS) module decomposes an image into a set of slots, which are then fused with queries to inject object-level visual priors into the object decoder. Additionally, a Class-Guided Slot Contrast (CGSC) mechanism is introduced to guide the slots toward domain-invariant yet class-relevant object features by leveraging contrastive learning with class prototypes. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed method.", "questions": "1. Why is CGSC not used during training on the source data?\n2. Can this method be applied to source-available domain adaptive object detection (DAOD)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The method achieves state-of-the-art performance across multiple datasets.\n\n2. The authors provide a solid theoretical analysis explaining why slot-based features can offer domain-invariant priors.", "weaknesses": "1. The HAS module introduces additional parameters and computation overhead. It would be beneficial to provide a comparison of speed and parameter size before and after adding HAS.\n\n2. The authors did not provide the performance of the source-only model after adding HAS. This omission makes it unclear whether the performance improvement stems from a stronger source-only model or from HAS enhancing the adaptation capability.\n\n3. HAS requires self-supervised pretraining on the COCO dataset. It would be useful to discuss the importance of this pretraining step.", "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": "gb3XQRdAoV", "reviewer": "Reviewer_GFAW", "summary": "The paper proposes CGSA, a novel source-free domain adaptive object detection framework that integrates object-centric learning into DETR-based models. It introduces two modules: Hierarchical Slot Awareness, which decomposes images into slot-based structural priors, and Class-Guided Slot Contrast, which aligns slots with class semantics through contrastive learning. The approach enables domain-invariant adaptation without source data. Experiments on multiple benchmarks show consistent and significant improvements over existing methods, supported by theoretical risk descent analysis.", "questions": "1. How sensitive is CGSA to the number of slots (n=5) or slot hierarchy depth?\n2. What is the computational overhead compared to standard DETR?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces CGSA, the first framework to combine Object-Centric Learning with Source-Free Domain Adaptive Object Detection, and integrates slot-based structural priors and class-guided contrastive alignment reasonably to achieve domain-invariant adaptation.\n2. Extensive experiments show that CGSA significantly outperforms prior methods, and the authors also provide theoretical analysis demonstrating risk reduction and stable convergence during adaptation.", "weaknesses": "1. Nevertheless, the proposed method remains highly dependent on the DETR framework, showing limited generalizability. Evaluating its effectiveness on other architectures, such as Faster R-CNN, is still desireable.\n2. There is no intuitive visualization or analysis of slot decomposition process.\n3. The risk analysis is mathematically sound but lacks empirical validation of key variables in authors' description, e.g., cosine margin gain or reconstruction consistency.", "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)