Associating Objects with Transformers for Video Object Segmentation (2106.02638v3)

Abstract: This paper investigates how to realize better and more efficient embedding learning to tackle the semi-supervised video object segmentation under challenging multi-object scenarios. The state-of-the-art methods learn to decode features with a single positive object and thus have to match and segment each target separately under multi-object scenarios, consuming multiple times computing resources. To solve the problem, we propose an Associating Objects with Transformers (AOT) approach to match and decode multiple objects uniformly. In detail, AOT employs an identification mechanism to associate multiple targets into the same high-dimensional embedding space. Thus, we can simultaneously process multiple objects' matching and segmentation decoding as efficiently as processing a single object. For sufficiently modeling multi-object association, a Long Short-Term Transformer is designed for constructing hierarchical matching and propagation. We conduct extensive experiments on both multi-object and single-object benchmarks to examine AOT variant networks with different complexities. Particularly, our R50-AOT-L outperforms all the state-of-the-art competitors on three popular benchmarks, i.e., YouTube-VOS (84.1% J&F), DAVIS 2017 (84.9%), and DAVIS 2016 (91.1%), while keeping more than $3\times$ faster multi-object run-time. Meanwhile, our AOT-T can maintain real-time multi-object speed on the above benchmarks. Based on AOT, we ranked 1st in the 3rd Large-scale VOS Challenge.

Associating Objects with Transformers for Video Object Segmentation: An Expert Overview

This paper explores the application of transformers to the semi-supervised video object segmentation (VOS) task, specifically in challenging multi-object scenarios. Traditional VOS methods often treat each object independently, thereby consuming significant computational resources. This paper introduces the Associating Objects with Transformers (AOT) framework, a novel approach to simultaneously match and decode multiple objects efficiently within a unified framework, making multi-object processing as efficient as single-object scenarios.

Key Contributions

1. Identification Mechanism:

AOT leverages an identification mechanism that assigns unique identities to targets, embedding them into a shared feature space. This allows multiple objects' matching and segmentation to be performed collectively. The identification mechanism is a fundamental element, placing objects in a high-dimensional feature space where their associations can be efficiently modeled.

2. Long Short-Term Transformer (LSTT):

The authors propose a Long Short-Term Transformer to construct hierarchical object matching and propagation processes. The LSTT architecture integrates both long-term and short-term attention mechanisms. Long-term attention aggregates information across temporal domains, while short-term attention ensures smooth temporal feature transitions between neighboring frames. This hierarchical approach improves model accuracy and allows the framework to scale efficiently.

Experimental Results

The proposed AOT framework shows superior performance on several VOS benchmarks:

• YouTube-VOS 2018/2019: The R50-AOT-L configuration achieves 84.1% in the $\mathcal{J}$ metric, outperforming state-of-the-art methods and maintaining a competitive processing speed of 14.9 FPS.
• DAVIS 2017 Validation and Testing: R50-AOT-L attains 84.9% and 79.6% on validation and testing splits, respectively, showcasing its efficiency and reliability in multi-object segmentation.
• DAVIS 2016: The method also excels in single-object scenarios on this benchmark, reaching 91.1% accuracy.

Implications and Future Directions

The AOT framework effectively addresses computational inefficiencies in existing VOS methods via its combined identification mechanism and transformer-based matching. This dual innovation enables integrated processing, significantly reducing the necessary resources for multi-object scenarios. The derived AOT models balance state-of-the-art performance with noteworthy efficiency, suggesting strong applicability in real-time video processing tasks, such as augmented reality and autonomous driving systems.

Future research can explore extending the identification mechanism to other multi-object tasks, including interactive VOS and video instance segmentation, potentially further exploiting transformers’ capabilities in these areas. As the field progresses, investigating stronger encoder-decoder architectures within the AOT framework could yield additional performance enhancements without sacrificing efficiency.