Self-Supervised Visual Representation Learning with Semantic Grouping (2205.15288v2)

Abstract: In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlying complex structure within scene-centric data; still, they commonly rely on hand-crafted objectness priors or specialized pretext tasks to build a learning framework, which may harm generalizability. Instead, we propose contrastive learning from data-driven semantic slots, namely SlotCon, for joint semantic grouping and representation learning. The semantic grouping is performed by assigning pixels to a set of learnable prototypes, which can adapt to each sample by attentive pooling over the feature and form new slots. Based on the learned data-dependent slots, a contrastive objective is employed for representation learning, which enhances the discriminability of features, and conversely facilitates grouping semantically coherent pixels together. Compared with previous efforts, by simultaneously optimizing the two coupled objectives of semantic grouping and contrastive learning, our approach bypasses the disadvantages of hand-crafted priors and is able to learn object/group-level representations from scene-centric images. Experiments show our approach effectively decomposes complex scenes into semantic groups for feature learning and significantly benefits downstream tasks, including object detection, instance segmentation, and semantic segmentation. Code is available at: https://github.com/CVMI-Lab/SlotCon.

An Analysis of "Self-Supervised Visual Representation Learning with Semantic Grouping"

The paper presents a novel approach named SlotCon, designed for learning visual representations from unlabeled scene-centric data through self-supervised learning. The paper critiques existing strategies that utilize handcrafted objectness priors or specific pretext tasks, which may limit the generalizability of learned representations. It introduces a mechanism that incorporates semantic grouping with contrastive learning, aiming to mitigate these limitations.

The approach leverages a dual-objective strategy: semantic grouping through a method termed "slot attention" and representation learning via contrastive learning objectives. Semantic grouping is achieved by assigning image pixels to a set of learnable prototypes through feature-space clustering. Attentive pooling over these features forms new slots, which adapt to each sample. These slots undergo contrastive learning to improve feature discriminability, enhancing the coherence of grouped pixels.

Key findings from SlotCon include its ability to decompose complex scene-centric images into semantically coherent groups effectively. This enhances the learning of object/group-level features that are beneficial for various downstream tasks, including COCO object detection, COCO instance segmentation, and semantic segmentation on datasets such as Cityscapes, PASCAL VOC, and ADE20K.

Salient Findings and Methodological Insights

• Contrastive Learning: The paper demonstrates that combining semantic grouping with contrastive learning bypasses issues stemming from pretext task dependencies. Unlike purely instance-discriminative approaches tailored for object-centric datasets like ImageNet, SlotCon offers a more nuanced approach that captures complex scenes.
• Numerical Efficacy: The experiments conducted show SlotCon outperforming existing methods in multiple tasks. With COCO as the pre-training dataset, SlotCon achieves an AP of 41.0 on COCO object detection and 76.2 mIoU on Cityscapes semantic segmentation, outperforming both pixel-level and existing object/group-level self-supervised techniques.
• Scalability Across Datasets: SlotCon is evaluated using multiple large-scale datasets. With larger datasets such as COCO+, SlotCon demonstrates an increase in transferability potential, nearly bridging the performance gap with methods pre-trained on the larger ImageNet-1K dataset.
• Unsupervised Semantic Segmentation: The approach is evaluated qualitatively and quantitatively for its ability to perform semantic grouping in complex scenes without labeled data, achieving mIoU of 18.26 on COCO-Stuff, surpassing previous unsupervised methods.

Theoretical and Practical Implications

The research provides insights into enhancing self-supervised learning frameworks by coupling pixel-level clustering with group-level semantics. It underscores the importance of data-driven methods over those depending on heuristic priors, notably presenting a flexible alternative applicable across varied real-world datasets.

Theoretically, SlotCon reinforces the hypothesis that meaningful semantic representations can emerge from the interplay between attentive semantic grouping and contrastive objectives. Practically, it illustrates a feasible path towards improved generalization in diverse real-world applications, potentially reducing reliance on labeled datasets.

Future Directions

1. Extension to Diverse Domains: SlotCon offers a foundation for self-supervised models tailored to domains like autonomous driving, where scene complexity is pronounced.
2. Exploration of Granularity: Investigating the influence of prototype number and distribution on the granularity of learned semantics could offer insights into better scaling across datasets with different complexity levels.
3. Integration with Real-Time Processing: Adapting SlotCon to operate efficiently under resource constraints might harbor potential benefits for edge computing applications in practice.

In conclusion, this work contributes significantly to the field of self-supervised visual representation learning, particularly for scene-centric images, and highlights a methodologically sound approach that serves as a stepping-stone for future research in this direction.