End-to-End Video Instance Segmentation with Transformers (2011.14503v5)

Abstract: Video instance segmentation (VIS) is the task that requires simultaneously classifying, segmenting and tracking object instances of interest in video. Recent methods typically develop sophisticated pipelines to tackle this task. Here, we propose a new video instance segmentation framework built upon Transformers, termed VisTR, which views the VIS task as a direct end-to-end parallel sequence decoding/prediction problem. Given a video clip consisting of multiple image frames as input, VisTR outputs the sequence of masks for each instance in the video in order directly. At the core is a new, effective instance sequence matching and segmentation strategy, which supervises and segments instances at the sequence level as a whole. VisTR frames the instance segmentation and tracking in the same perspective of similarity learning, thus considerably simplifying the overall pipeline and is significantly different from existing approaches. Without bells and whistles, VisTR achieves the highest speed among all existing VIS models, and achieves the best result among methods using single model on the YouTube-VIS dataset. For the first time, we demonstrate a much simpler and faster video instance segmentation framework built upon Transformers, achieving competitive accuracy. We hope that VisTR can motivate future research for more video understanding tasks.

End-to-End Video Instance Segmentation with Transformers

The paper "End-to-End Video Instance Segmentation with Transformers" introduces VisTR, a novel framework leveraging Transformers for video instance segmentation (VIS). VIS is a multifaceted computer vision task involving the classification, segmentation, and tracking of object instances across video frames—presenting unique challenges distinct from static image segmentation.

Approach and Methodology

VisTR redefines the conventional pipeline by framing VIS as a direct end-to-end sequence prediction problem. This model processes a sequence of video frames and outputs corresponding masks for each instance. At its core is a new strategy for instance sequence matching and segmentation, significantly simplifying the traditional complex approaches.

Key Components

1. Transformers: Inspired by their success in NLP and evolving applications in vision tasks, Transformers facilitate the modeling of spatial and temporal dependencies across video frames. VisTR utilizes a Transformer encoder-decoder architecture to handle the entire video clip as input, providing a clean and efficient framework.
2. Instance Sequence Matching: This component employs a bipartite matching strategy, utilizing the Hungarian algorithm to align predicted and ground truth sequences optimally. Importantly, VisTR treats the VIS task as executing similarity learning over sequences rather than independent frames, thus enhancing coherence over time.
3. Instance Sequence Segmentation: With this, VisTR applies self-attention mechanisms across multiple frames to aggregate instance features, leveraging 3D convolutions to produce a cohesive mask sequence. This approach ensures that the model harnesses temporal information effectively.

Performance and Results

VisTR has shown impressive performance on the YouTube-VIS dataset, achieving 40.1% mask mAP at 57.7 FPS with a ResNet-101 backbone. It surpasses existing VIS models in speed and accuracy, particularly those reliant on complex, multi-stage pipelines like MaskTrack R-CNN and STEm-Seg. These results underscore VisTR's ability to deliver competitive performance in a significantly streamlined manner.

Implications and Future Directions

The introduction of Transformers into VIS exemplifies a broader trend within computer vision towards unified, sequence-based models. This paradigm shift holds potential for simplifying and enhancing a variety of vision tasks, potentially enabling Transformer-based architectures across diverse modalities such as video, images, and point cloud data.

VisTR's architectural simplicity and efficiency pave the way for further research into Transformer applications for broader video-related tasks. Future developments could focus on extending this approach to more complex scenarios and exploring Transformer scalability across larger datasets.

Conclusion

This paper provides a compelling demonstration of the efficacy of Transformers for VIS, offering a streamlined approach that merges segmentation and tracking tasks into a cohesive framework. Such an approach could inform ongoing efforts to harness the capabilities of Transformers for other dynamic, sequence-based computer vision tasks.