Guided Latent Slot Diffusion for Object-Centric Learning
(2407.17929)Abstract
Slot attention aims to decompose an input image into a set of meaningful object files (slots). These latent object representations enable various downstream tasks. Yet, these slots often bind to object parts, not objects themselves, especially for real-world datasets. To address this, we introduce Guided Latent Slot Diffusion - GLASS, an object-centric model that uses generated captions as a guiding signal to better align slots with objects. Our key insight is to learn the slot-attention module in the space of generated images. This allows us to repurpose the pre-trained diffusion decoder model, which reconstructs the images from the slots, as a semantic mask generator based on the generated captions. GLASS learns an object-level representation suitable for multiple tasks simultaneously, e.g., segmentation, image generation, and property prediction, outperforming previous methods. For object discovery, GLASS achieves approx. a +35% and +10% relative improvement for mIoU over the previous state-of-the-art (SOTA) method on the VOC and COCO datasets, respectively, and establishes a new SOTA FID score for conditional image generation amongst slot-attention-based methods. For the segmentation task, GLASS surpasses SOTA weakly-supervised and language-based segmentation models, which were specifically designed for the task.
Overview
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The paper introduces GLASS, a novel approach for enhancing object-centric learning (OCL) by using guided latent slot diffusion, aligning slot attention mechanisms with whole objects in complex datasets.
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GLASS integrates diffusion models with slot attention, employs a guidance loss mechanism using pseudo-ground-truth semantic masks, and demonstrates substantial performance improvements on VOC and COCO datasets.
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Empirical evaluations show that GLASS achieves significant gains in object discovery, conditional image generation, and object-level property prediction, representing a new state-of-the-art in these areas.
A Formal Analysis of "Guided Latent Slot Diffusion for Object-Centric Learning"
The paper "Guided Latent Slot Diffusion for Object-Centric Learning" introduces a sophisticated approach named GLASS for enhancing object-centric learning (OCL) via guided latent slot diffusion. This method utilizes generated captions as a guiding signal to better align slot attention mechanisms with objects, addressing a notable challenge in OCL methodologies where slots frequently bind to object parts rather than whole objects in complex real-world datasets.
Core Contributions
The primary contributions of this research include:
Integration of Diffusion Models with Slot Attention: GLASS capitalizes on generated images and learns the slot attention module within this generated image space. This innovative learning paradigm allows the model to repurpose pre-trained diffusion decoders—specifically, Stable Diffusion models—as semantic mask generators based on generated captions.
Guidance Loss: A novel guidance loss mechanism is introduced, leveraging pseudo-ground-truth semantic masks refined from cross-attention maps of the diffusion model. This guidance directs the slots to encapsulate whole objects rather than fragmented parts, facilitating improved slot embeddings that are more aligned with human-perceived object boundaries.
Empirical Performance: The effectiveness of GLASS is empirically validated on challenging benchmarks like VOC and COCO datasets. For object discovery, GLASS demonstrates substantial improvements, achieving approximately +35% and +10% relative gains in mIoU over previous state-of-the-art (SOTA) methods on the VOC and COCO datasets, respectively. Moreover, GLASS defines a new SOTA in Fréchet Inception Distance (FID) scores for conditional image generation among slot-attention-based approaches.
Technical Details
Slot Attention Mechanism
The slot attention architecture in GLASS follows an iterative refinement process using GRU networks. A set of slots initialized randomly or via a learned function iteratively aggregate features from an encoded input image through standard dot-product attention, subsequently updated to represent different parts of the image dynamically while maintaining constraints for object-level decomposition.
Latent Diffusion Models
Unlike conventional image generation methods, latent diffusion models adopted in GLASS perform iterative denoising from a noise vector conditioned on text embeddings derived from generated captions. The Stable Diffusion model emerges as a robust decoder capable of reconstructing high-fidelity images from slot representations.
Pseudo-ground-truth Generation
For generating semantic masks, cross-attention maps are extracted from the U-Net architecture within the diffusion model, representing spatial object relationships. These maps are refined using self-attention techniques, resulting in high-quality pseudo-ground-truth masks used for guiding the slots during training.
Performance Evaluation
GLASS's superiority is evident across multiple OCL tasks—object discovery, conditional image generation, and object-level property prediction:
Object Discovery: GLASS excels in object detection and segmentation tasks with marked improvements in mIoU, mBO$i$, mBO$c$, CorLoc, and DetRate metrics. Qualitative analyses reveal that GLASS produces more coherent and aligned object masks compared to existing methods like DINOSAUR and StableLSD, with cleaner object boundaries and less fragmentation.
Conditional Generation: In terms of image reconstruction, GLASS significantly outperforms StableLSD with better FID scores on both VOC and COCO datasets, indicating more realistic and high-fidelity image generation from learned slots.
Object-level Property Prediction: Despite a minor trade-off in top-1 accuracy for property prediction, GLASS achieves vastly superior detection rates, affirming that its slots encapsulate more comprehensive and meaningful object-level representations.
Theoretical and Practical Implications
The adoption of generative models for learning latent object representations presents profound theoretical implications. By demonstrating that synthetic training can generalize effectively to real-world scenarios, GLASS indicates a paradigm shift in OCL methods' scalability and applicability to diverse, complex datasets. Practically, the improved object representation capability facilitates advancements in various downstream tasks such as segmentation, image editing, and more autonomous, robust scene understanding in AI systems.
Future Directions
The research opens several avenues for further exploration:
Instance-level Detection:
Enhancing GLASS to discriminate between instances within a semantic class would further refine its applicability in more granular object detection scenarios.
Broader Applications:
Applying GLASS to varied datasets beyond VOC and COCO could validate its versatility and robustness across different domains and object categories.
In summary, the GLASS framework marks a substantial advancement in object-centric learning by effectively leveraging diffusion models and generating informative guidance signals. This approach stands out for its empirical robustness and theoretical soundness, offering a promising trajectory for future research in AI-driven scene understanding and object representation.
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