LinSlot: Exploiting Linear Representation hypothesis for unsupervised attribute discovery from slot based object representation

Sanket Gandhi; Utkarsh Giri; Varun Subramanium; Rohan Paul; Parag Singla

cs.CVOct 7, 2026

LinSlot: Exploiting Linear Representation hypothesis for unsupervised attribute discovery from slot based object representation

Authors: Sanket Gandhi, Utkarsh Giri, Varun Subramanium, Rohan Paul, Parag Singla

Organizations: Yardi School of AI, IIT Delhi · Department of Computer Science and Engineering, IIT Delhi

Abstract

This paper studies the problem of learning disentangled representations of objects and their attributes from raw, unstructured image data. Slot-based methods have shown considerable success in unsupervised learning of object representations from images. Block-slot attention-based methods extend this framework to attribute representations by assuming a uniform factorization of object representations into attributes, which may be suboptimal and consequently limit the quality of the learned representations. We therefore investigate a framework for jointly discovering object and attribute representations. Our key contribution is leveraging the Linear Representation Hypothesis (LRH), which postulates that composable concepts can be represented as linearly additive subspaces in slot representations. Based on this insight, we propose a probabilistic model connecting images, slots (objects), and blocks (attributes). We present an architecture that leverages block attention to connect attribute representations to slots and incorporates LRH in both object and attribute representation spaces. This architecture effectively optimizes the Evidence Lower Bound (ELBO) of the proposed graphical model. Our experiments demonstrate (i) effective discovery of disentangled object and attribute representations, (ii) empirical evidence for LRH in slot space, and (iii) the ability to perform image editing owing to the disentangled and interpretable nature of the learned representations. Our experiments on multiple datasets demonstrate improvements in DCI scores over state-of-the-art methods.

Figures & tables

Panel of Figure 1 — Figure 1: Overview of the proposed representation and generative model.

Figure 3: Overview of the inference: The image is encoded into spatial features $\mathbf{E}$ , from which slots $\mathbf{S}$ are inferred via slot attention. For each slot, projected features $\mathbf{v}$ are used to iteratively refine $M$ attribute blocks through competitive attention, with each block constrained to a linear combination of shared concept vectors. The resulting blocks serve as disentangled attribute representations.

Figure 4: Object Manipulation via delta directions: Delta direction is estimated by subtracting slot of object indicated from first image from slot of the object indicated in second image. This delta direction is then added to the object indicated in the third image. The fourth image is decoded by slots manipulated slots from third image. First three images are reconstruction.

	CLEVR Easy		CLEVR Hard		CLEVR-Tex		MOVi-C
Method	Slot Attn.	SLATE+	Slot Attn.	SLATE+	SLATE+	Slot Diff.	CODA
Same $\delta$	0.6610	0.7465	0.6024	0.6511	0.5618	0.6356	0.2230
Different $\delta$	0.2232	0.1647	0.1898	0.1493	0.1498	0.1719	0.1020

Table 1: Cosine similarity between two estimations of the same delta direction.

Figure 5: Quantitative Results: Comparison of DCI and FG-ARI scores for our model and baselines across all datasets.

Figure 6: Qualitative Results: Comparison of attribute swapping results between our model and SysBinder. Blue arrows indicate the objects participating in the swaps. Recon. denotes the image reconstructed by the model.

Table 7

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Module	Hyperparameter	Dataset
		CLEVR	CLEVR	CLEVR
		Easy	Hard	Tex
General	Batch Size	64	64	64
	Image Size	128	128	128
	Training Steps	200K	260K	315K
	Encoder Learning Rate	0.0001	0.0001	0.0001

Appendix

Table 4: Hyperparameters of training for CLEVR-Easy, CLEVR-Hard, and CLEVR-Tex datasets.

Method	FG-ARI
SLATE	0.8166
SLATE+	0.9369

Appendix

Table 5: SLATE vs SLATE+ Results on CLEVR-Easy

Dataset (model)	Factor	#Values	Stable rank	Part. ratio
CLEVR-Easy (Slot Attention, $d_{\text{slot}}{=}64$ )	Position	cont.	$3.21\pm 0.38$	$5.58\pm 0.53$
	Color	8	$2.93\pm 0.62$	$4.91\pm 1.46$
	Shape	3	$2.06\pm 0.16$	$3.70\pm 0.48$
CLEVR-Hard (SLATE+, $d_{\text{slot}}{=}192$ )	Position	cont.	$3.89\pm 0.05$	$8.08\pm 0.04$
	Color	137	$3.82\pm 0.11$	$7.24\pm 0.34$
	Shape	3	$2.79\pm 0.19$	$5.37\pm 0.52$

Appendix

Table 6: Stable rank and participation ratio of the delta-direction subspace of each factor (mean

\pm

std over seeds).

Figure 8: Steering slots along principal axes of the delta space: We steer slots by adding a scaled multiple $\alpha$ of a principal axis computed from the delta directions associated with a particular factor. The slots corresponding to the objects highlighted by red boxes in the first column are steered, with $\alpha$ increasing from left to right. The resulting images exhibit meaningful and smooth changes along the corresponding factor, consistently across different objects, datasets, and methods, including Slot Attention, SLATE+, and Slot Diffusion.

Explore similar work

May 22, 2026cs.CV

SLIP-RS: Structured-Attribute Language-Image Pre-Training for Remote Sensing Object Detection

Existing language-image pre-training for remote sensing object detection is constrained by Monolithic Label Learning, which relies on exhaustively enumerating open-set categories via black-box data to acquire fine-grained representations, creating a dependency incompatible with the domain's inherent data scarcity. To transcend this bottleneck, we propose SLIP-RS, establishing a Structured-Attribute Decoupling Paradigm that maps the open-ended category space into a finite, physically meaningful attribute space, unlocking fine-grained discriminability via explicit structural logic. This paradigm is realized via two technical pillars: (1) Structured-Attribute Contrastive Learning, which enforces the learning of decoupled intrinsic visual logic via combinatorial attribute augmentation; and (2) Conformal Attribute Reliability Engine, which leverages conformal prediction theory to rigorously distill high-fidelity supervision from noisy sources, yielding RS-Attribute-15M, the largest dataset with over 15 million attribute annotations. Extensive experiments demonstrate that SLIP-RS establishes unprecedented performance in fine-grained detection and cross-domain generalization, validating structured attributes as a vital foundation for remote sensing. Code: https://github.com/facias914/SLIP-RS.

Chenxu Wang, Yuxuan Li, Yunheng Li +3

1VCIP, CS, Nankai University · 2NKIARI, Shenzhen Futian · 3National University of Defense Technology

Open-Vocabulary Object Detection Remote Sensing Image Understanding Remote Sensing Small Object Detection Representation Learning Multimodal Pretraining Multimodal Contrastive Learning

Ask PDF

Jul 9, 2026cs.CV

HSA: Hierarchical Slot Attention for Multi-granularity Scene-Decomposition

Slot attention is a powerful framework for object-centric learning, decomposing visual scenes into latent slots through iterative competitive attention. However, existing methods share two critical limitations: they decompose scenes into a flat set of slots at a single granularity, and this decomposition is based on appearance rather than semantics. Yet humans understand scenes through semantic hierarchies: separating foreground from background, recognizing object categories, and identifying individual instances. Crucially, such semantic hierarchies cannot emerge without supervision, because category names are human constructs, not visual patterns. We propose Hierarchical Slot Attention (HSA), which learns multi-granularity semantic scene decomposition from a single model. HSA decomposes scenes at three levels: holistic (foreground/background), semantic (object categories), and panoptic (individual instances). Using only 10% labeled data, combined with hierarchical alignment loss, HSA learns all three levels jointly. We further introduce grouping purity and containment to measure whether the hierarchy is encoded in representation space, not just output masks. Experiments on COCO and PASCAL VOC demonstrate that HSA outperforms the strongest flat baseline by up to \textbf{

+

41.5} ARI at holistic, \textbf{

+

14.6} at semantic, and \textbf{

+

10.4} at panoptic level on COCO, with even larger gains on Pascal VOC, while requiring a single model instead of three. Code will be made available upon acceptance.

Neelu Madan, Rongzhen Zhao, Andreas Mogelmose +4

1Aalborg University, Denmark · 2Pioneer Centre for AI, Denmark · 3Aalto University, Finland +2

Panoptic Segmentation Hierarchical Representation Learning Object-Centric Representation Learning Slot Attention

Ask PDF

Sep 14, 2026cs.CV

Does Attention-Guided Masking Really Help Object Discovery in Object-Centric Learning?

Object-Centric Learning (OCL) aims to decompose images into objects without human annotations. A major family of mainstream methods uses Slot Attention to aggregate image features into object-level representations and then from them reconstructs masked image content, i.e., Random Masking (RM), to provide self-supervision. The recent method DIAS simply masks image patches at uniform randomness yet achieves competitive object discovery accuracy. Since attention during aggregation already possesses object discovery ability, we explore using it to develop a better image patch masking strategy, i.e., Attention Guided Masking (AGM), thereby providing better self-supervision. Results on six recognized datasets show that AGM does not always outperform RM. Under unconditional slot initialization, AGM substantially improves background segmentation on datasets with realistic textures (COCO and VOC); Regardless of conditional or unconditional slot initialization and across datasets, foreground object discovery remains comparable or decreases. We suggest peer researchers in the OCL community that attempts to exploit internal attention semantics to improve OCL with masked decoding are risky. Our source code, model checkpoints and evaluation logs is available on https://github.com/und-entropy/Does-Attention-Guided-Masking-Really-Help-Object-Discovery-in-Object-Centric-Learning-.

Youliang Tao, Yanhua Han, Bin Zhao +3

School of Artificial Intelligence, Guilin University of Electronic Technology · Department of Computer Science, Aalto University · Center for Machine Vision and Signal Analysis, University of Oulu +1

Object-Centric Representation Learning Slot Attention Masked Image Modeling

Ask PDF