Organizations: Department of Electrical and Computer Engineering, Democritus University of Thrace, Greece · Institute for Language and Speech Processing, Athena Research Center, Greece · Department of Informatics & Telecommunications, National and Kapodistrian University of Athens, Greece · Archimedes, Athena Research Center, Greece
Slot-based object-centric learning represents an image as a set of latent slots with a decoder that combines them into an image or features. The decoder specifies how slots are combined into an output, but the slot set is typically fixed: the number of slots is chosen upfront and slots are only refined. This can lead to multiple slots competing for overlapping regions of the same entity rather than focusing on distinct regions. We introduce slot merging: a drop-in, lightweight operation on the slot set that merges overlapping slots during training. We quantify overlap with a Soft-IoU score between slot-attention maps and combine selected pairs via a barycentric update that preserves gradient flow. Merging follows a fixed policy, with the decision threshold inferred from overlap statistics, requiring no additional learnable modules. Integrated into the established feature-reconstruction pipeline of DINOSAUR, the proposed method improves object factorization and mask quality, surpassing other adaptive methods in object discovery and segmentation benchmarks.
Figures & tables
Figure 1: We introduce a merge operator over the slot set that adaptively refines factorization, producing coherent object-level representations.
Figure 2: Illustration of the proposed pipeline.
Figure 3: Overview of the proposed training pipeline and merging mechanism. After Slot Attention produces refined slots and attention maps, pairwise Attention-IoU scores are computed to identify the most overlapping slot pair. The selected slots are merged via barycentric aggregation and their attention maps are summed. The procedure is repeated iteratively until no overlap exceeds the threshold, yielding a refined slot set passed to the decoder.
Figure 4: Evolution of slot representations during training (left to right). Slot assignments transition from fragmented and unstable to progressively specialized and object-aligned. After stabilization (right), slot merging is enabled to resolve overlap between slots.
Figure 5: Qualitative results on COCO and PASCAL VOC.
VOC
MOVi-C
mBO i
mBO c
mIoU
mBO i
mIoU
Baseline
44.0 ± 1.9
51.2 ± 1.9
–
42.4
–
Merging during Inference
46.01 ± 0.7
52.68 ± 0.65
44.51 ± 0.55
44.53 ± 1.7
43.81 ± 1.6
Merging during Training
47.83 ± 0.17
55.34 ± 0.27
46.01 ± 0.18
48.35 ± 1.32
47.43 ± 1.26
Table 1: Comparison between inference-only and training-integrated slot merging on VOC and MOVi-C, evaluated with mBO (instance and class). Results are averaged over 3 seeds.
VOC
MOVi-C
mBO i
mBO c
mIoU
mBO i
mIoU
With Detach
45.75 ± 0.28
53.13 ± 0.42
43.97 ± 0.22
45.39 ± 2.95
44.46 ± 2.9
Without Detach
47.83 ± 0.17
55.34 ± 0.27
46.01 ± 0.18
48.35 ± 1.32
47.43 ± 1.26
Table 2: Ablation on gradient detachment in the barycentric slot merging operator during training on VOC and MOVi-C. Results are the mean and standard deviation of the decoder’s metrics over 3 seeds.
mBO i
mBO c
mIoU
Without Aggregation
47.38 ± 0.52
54.83 ± 0.58
45.53 ± 0.49
With Aggregation
47.83 ± 0.17
55.34 ± 0.27
46.01 ± 0.18
Table 3: Effect of attention map aggregation during slot merging. Results are averaged over 3 seeds.
Training / Inference
VOC
COCO
MOVi-C
MOVi-E
Mean
1.67 / 1.73
0.69 / 0.82
2.2 / 2.2
7.96 / 7.91
Std
1.0 / 0.98
0.76 / 0.81
1.32 / 1.32
2.13 / 2.08
Table 4: Merging statistics during training and inference across all four datasets, reporting the mean and standard deviation of the number of merged slots per image.
VOC
MOVi-C
Epoch
mBO i
mBO c
mIoU
Epoch
mBO i
mIoU
90
46.15
52.91
44.65
50
47.05
46.19
80
46.96
54.10
45.25
40
46.83
46.00
70
47.83
55.33
46.01
30
48.35
47.43
Table 5: Influence of the training epoch at which slot merging is activated, evaluated on VOC and MOVi-C.
VOC
COCO
Method
mBO i
mBO c
mBO i
mBO c
SA [ 24 ] † NeurIPS’20
24.6
24.9
17.2
19.2
SLATE [ 32 ] † ICLR’22
35.9
41.5
29.1
33.6
CAE [ 26 ] † TMLR
32.9 ± 0.9
37.4 ± 1.0
–
–
Rotating Features [ 25 ] † NeurIPS’23
40.7 ± 0.1
46.0 ± 0.1
–
–
DINOSAUR-MLP [ 31 ] † ICLR’23
39.5 ± 0.1
40.9 ± 0.1
27.7 ± 0.2
30.9 ± 0.2
Table 6: Comparison with object-centric methods on the VOC and COCO datasets. Results for our approach are reported as mean ± standard deviation over 3 seeds, using a ViT-B/16 backbone and 224×224 input resolution. † indicates results taken from [ 17 ] . Otherwise results are from the original papers.
MOVi-C
MOVi-E
Method
mBO i
mIoU
mBO i
mIoU
SA [ 24 ] † NeurIPS’20
26.2 ± 1.0
–
24.0 ± 1.2
–
SLASH [ 19 ] † CVPR’23
–
27.7 ± 5.9
–
–
SLATE [ 32 ] † ICLR’22
39.4 ± 0.8
37.8 ± 0.7
30.2 ± 1.7
28.6 ± 1.7
DINOSAUR-MLP [ 31 ] † ICLR’23
39.1 ± 0.2
–
35.5 ± 0.2
–
DINOSAUR [ 31 ] † ICLR’23
42.4
–
–
–
Table 7: Comparison with object-centric methods on MOVi-C and MOVi-E. Results for our approach are reported as mean ± standard deviation over 3 seeds. † indicates results taken from [ 17 ] . Otherwise results are from the original papers.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 6: Visualizations of the iterative slot merging process. Starting from slot representations produced by Slot Attention, the algorithm repeatedly merges the pair of slots with the highest Soft-IoU overlap. The final merged slot set is then passed to the decoder for reconstruction.
Figure 7: (a) Data-driven estimation of threshold from the histogram of pairwise Soft-IoU scores using the maximum perpendicular distance criterion. (b) Sensitivity analysis of the merging threshold on the VOC dataset measured using mBOi and mBOc .
Encoder
Method
mBO i
mBO c
mIoU
DINO
DINOSAUR
44.0
51.2
–
Ours
47.8
55.3
46.0
DINOv2
DINOSAUR
43.2
47.3
42.1
Ours
44.4
50.5
43.2
MAE
DINOSAUR
37.8
42.7
36.5
Ours
38.3
43.4
36.7
Appendix
Table 8: Different encoders comparison. DINO [ 3 ] and MAE [ 15 ] use ViT-B/16 architecture [ 6 ] while DINOv2 [ 27 ] employs ViT-B/14.
Method
VOC
MOViC
DINOSAUR
∼ 53 minutes
∼ 4h 44 minutes
Ours
∼ 53.5 minutes
∼ 4h 50 minutes
Appendix
Table 9: Training time comparison between DINOSAUR and our method.
Table 10: Comparison with object-centric methods on VOC, COCO, MOVi-C, and MOVi-E datasets. Results for our approach are reported as mean ± standard deviation over 3 seeds. † indicates results taken from [ 17 ] . Otherwise results are from the original papers. The best results are shown in bold and the second-best results are underlined .
Object-Centric Representation Learning studies visual models that organize scenes into distinct objects and their properties, positions, and relations.