Emergence of Segmentation with Minimalistic White-Box Transformers (2308.16271v1)

Abstract: Transformer-like models for vision tasks have recently proven effective for a wide range of downstream applications such as segmentation and detection. Previous works have shown that segmentation properties emerge in vision transformers (ViTs) trained using self-supervised methods such as DINO, but not in those trained on supervised classification tasks. In this study, we probe whether segmentation emerges in transformer-based models solely as a result of intricate self-supervised learning mechanisms, or if the same emergence can be achieved under much broader conditions through proper design of the model architecture. Through extensive experimental results, we demonstrate that when employing a white-box transformer-like architecture known as CRATE, whose design explicitly models and pursues low-dimensional structures in the data distribution, segmentation properties, at both the whole and parts levels, already emerge with a minimalistic supervised training recipe. Layer-wise finer-grained analysis reveals that the emergent properties strongly corroborate the designed mathematical functions of the white-box network. Our results suggest a path to design white-box foundation models that are simultaneously highly performant and mathematically fully interpretable. Code is at \url{https://github.com/Ma-Lab-Berkeley/CRATE}.

• The paper introduces CRATE, a white-box transformer that achieves emergent segmentation solely through supervised training.
• It employs multi-head subspace self-attention and ISTA-based sparse encoding to map high-dimensional inputs into compact, interpretable features.
• Experimental results on ImageNet-21k, PASCAL VOC, and COCO demonstrate that CRATE produces segmentation maps comparable to or better than DINO-trained models.

Emergence of Segmentation with Minimalistic White-Box Transformers

This paper explores the emergent capability of object segmentation within transformer-based architectures when trained with supervised learning, focusing on a model referred to as Encoding Rate CRATE (ERC). The research primarily investigates whether the ability to segment images inherently arises in such architectures without relying on complex self-supervised learning frameworks like DINO, which have been traditionally used to achieve similar results.

Key Contributions

The authors present a transformer architecture termed "white-box" due to its interpretable design. The CRATE framework emphasizes modeling data's low-dimensional structures through an interpretable layer design that utilizes subspace projections and sparse representations. The main contributions and insights derived from this work are:

1. White-Box Design Advantage: By using a structured, interpretable architecture, CRATE emerges with segmentation abilities through conventional supervised training, as opposed to the intricate self-supervised methods necessary for other vision transformers like ViT with DINO.
2. Subspace and Sparse Encoding: The architectural design of CRATE emphasizes multi-head subspace self-attention (MSSA) and iterative shrinkage-thresholding algorithms (ISTA) to map high-dimensional visual input into compact feature representations. These components are theorized to incrementally optimize sparse rate reduction and improve the segmentation properties of the transformer.
3. Segmentation Without Self-Supervision: Experimental analyses indicate CRATE's superiority in producing segmentation maps qualitatively similar to or better than those from DINO-trained models but without the self-supervised training complexities. This challenges the research community's prior belief that segmentation capabilities in vision transformers are tightly coupled with self-supervised paradigms.

Methodology and Results

• Experimental Setup: The experiments were conducted using CRATE models at various scales (-S/8 and -B/8), trained on extensive visual data (ImageNet-21k) and evaluated on datasets like PASCAL VOC and COCO, employing metrics such as mean Intersection over Union (mIoU) for segmentation quality.
• Self-Attention and PCA Analysis: Attention maps generated by CRATE highlight clear segmentation of visual objects, confirmed by principal component analysis of patch representations aligning with object boundaries in a manner more structured than that of supervised ViT models.
• Quantitative Evaluation: CRATE significantly outperformed the ViT in segmentation performance, including mIoU on PASCAL VOC, establishing its capability in producing high-quality segmentation masks just through supervised learning.
• Architectural Ablations: Further studies confirmed the critical role of MSSA and ISTA blocks in achieving the emergent segmentation. Replacing these components with traditional alternatives reduced the model's segmentation efficacy.

Implications and Future Directions

• Theoretical Insights: The work provides an intriguing perspective on designing network architectures that are mathematically interpretable and empirically robust across tasks traditionally dominated by black-box designs.
• Practical Relevance: CRATE’s success in segmenting objects using regular supervised training could streamline applications in domains requiring straightforward, interpretable AI solutions without sacrificing performance efficiency anticipated from complex frameworks.
• Future Trajectories: This paper prompts further exploration into white-box transformer designs and their applicability across various modalities beyond vision tasks. Additionally, enhancing such architectures to achieve comparable or superior results to self-supervised models like DINO without extensive modifications remains an open research avenue.

In summary, this paper positions CRATE as a promising, interpretability-driven alternative to conventional transformer designs within computer vision, especially for tasks involving segmentation, leveraging simplicity in training mechanisms while maintaining competitive performance.