Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation (2311.17893v2)

Abstract: In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS). Our key insight is that the inherent structural dependencies present in DINO-pretrained Transformers can be leveraged to establish robust spatio-temporal correspondences in videos. Furthermore, simple clustering on this correspondence cue is sufficient to yield competitive segmentation results. Previous self-supervised VOS techniques majorly resort to auxiliary modalities or utilize iterative slot attention to assist in object discovery, which restricts their general applicability and imposes higher computational requirements. To deal with these challenges, we develop a simplified architecture that capitalizes on the emerging objectness from DINO-pretrained Transformers, bypassing the need for additional modalities or slot attention. Specifically, we first introduce a single spatio-temporal Transformer block to process the frame-wise DINO features and establish spatio-temporal dependencies in the form of self-attention. Subsequently, utilizing these attention maps, we implement hierarchical clustering to generate object segmentation masks. To train the spatio-temporal block in a fully self-supervised manner, we employ semantic and dynamic motion consistency coupled with entropy normalization. Our method demonstrates state-of-the-art performance across multiple unsupervised VOS benchmarks and particularly excels in complex real-world multi-object video segmentation tasks such as DAVIS-17-Unsupervised and YouTube-VIS-19. The code and model checkpoints will be released at https://github.com/shvdiwnkozbw/SSL-UVOS.

Insights into Self-supervised Video Object Segmentation

This paper introduces a novel approach to self-supervised video object segmentation, emphasizing the utility of attention mechanisms and hierarchical clustering algorithms to achieve efficient object segmentation without reliance on traditional annotation methods. The authors examine the efficacy of their method on both synthetic and real-world datasets, including MOVi-E, DAVIS-17, and YouTube-VIS-19.

The essence of the proposed methodology lies in leveraging spatio-temporal attention maps derived from video inputs. Each attention map is initially treated as an individual cluster, a series of which are then iteratively merged based on proximity using the KL-divergence metric to form a refined object representation. This merging process is facilitated by hierarchically clustering the attention maps, leading to effective object segmentation masks. The approach notably reduces computational demands by selectively sampling frames to compute cross-attention, thereby maintaining performance while optimizing memory usage.

On the experimental front, this approach demonstrates robust performance across multiple benchmarks. Notably, it reports a mean Intersection over Union (mIoU) of 74.8 and a Foreground Adjusted Rand Index (FG-ARI) of 73.3 on datasets such as FBMS-59, outperforming benchmarks that employ optical flow techniques, particularly in single-object tasks.

The paper also explores ablation studies that test various pretrained backbones, specifically contrasting models from the DINO and DINOv2 families with different patch sizes. The findings indicate that models with smaller patch sizes generally perform better due to the production of more granular segmentation. Additionally, the impact of using various ratios of key frame sampling is assessed, revealing that even sparse sampling can yield competitive results, facilitating significant inference speedups.

From a theoretical standpoint, the research uncovers promising insights into the generalization ability of attention-based models in video segmentation, suggesting this approach can adapt flexibly to varying scenarios without needing explicit optical flow information. This positions the method as particularly versatile across differing video content complexities and dynamic environments.

Looking towards future developments, this work suggests potential expansions, including optimizing clustering via advanced machine learning techniques such as pruning and quantization for enhanced efficiency. Moreover, it opens avenues for exploring more sophisticated hierarchical structures that could further refine the granularity of segmentation outcomes.

In conclusion, this paper presents a compelling case for attention-based, self-supervised learning frameworks as potent tools for video object segmentation, eliminating dependencies on large annotated datasets while achieving competitive performance metrics. Such approaches could significantly influence future research directions in the domains of autonomous systems and video analysis.