Temporally Efficient Vision Transformer for Video Instance Segmentation (2204.08412v1)

Abstract: Recently vision transformer has achieved tremendous success on image-level visual recognition tasks. To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient Vision Transformer (TeViT) for video instance segmentation (VIS). Different from previous transformer-based VIS methods, TeViT is nearly convolution-free, which contains a transformer backbone and a query-based video instance segmentation head. In the backbone stage, we propose a nearly parameter-free messenger shift mechanism for early temporal context fusion. In the head stages, we propose a parameter-shared spatiotemporal query interaction mechanism to build the one-to-one correspondence between video instances and queries. Thus, TeViT fully utilizes both framelevel and instance-level temporal context information and obtains strong temporal modeling capacity with negligible extra computational cost. On three widely adopted VIS benchmarks, i.e., YouTube-VIS-2019, YouTube-VIS-2021, and OVIS, TeViT obtains state-of-the-art results and maintains high inference speed, e.g., 46.6 AP with 68.9 FPS on YouTube-VIS-2019. Code is available at https://github.com/hustvl/TeViT.

• The paper introduces a nearly convolution-free framework using messenger shift and spatiotemporal query interaction to efficiently model temporal context in videos.
• It achieves state-of-the-art performance on benchmarks like YouTube-VIS, with an AP of 46.6 and speeds up to 68.9 FPS.
• The method enhances segmentation accuracy and efficiency, setting a new precedent for transformer-based video understanding in challenging scenarios.

Towards Temporally Efficient Vision Transformers for Video Instance Segmentation

The development of video instance segmentation (VIS) techniques has garnered significant attention as a crucial aspect of contemporary video understanding tasks. The paper entitled “Temporally Efficient Vision Transformer for Video Instance Segmentation” introduces the Temporally Efficient Vision Transformer (TeViT), which sets forth an efficient approach to incorporating temporal context within video data. TeViT effectively leverages transformer architectures, known for their prowess in long-range context modeling, to address temporal dynamics inherent in video data.

Key Contributions

The paper distinguishes itself by presenting a nearly convolution-free framework that introduces two novel mechanisms: a messenger shift mechanism for early temporal context fusion and a spatiotemporal query interaction mechanism. These mechanisms are embedded within TeViT's transformer backbone and VIS head, respectively.

1. Messenger Shift Mechanism: Integrated at the backbone stage, this mechanism is nearly parameter-free, designed to facilitate early frame-level temporal fusion. By using messenger tokens within the backbone, temporal contexts across frames are efficiently aggregated without incurring significant computational overhead or additional parameters.
2. Spatiotemporal Query Interaction: The VIS head has been engineered to establish a one-to-one correspondence between video instances and queries, utilizing shared multi-head self-attention parameters. This interaction permits effective instance-level temporal context utilization, enhancing the model’s temporal modeling capacity.

Performance and Results

On benchmark datasets, TeViT showcases state-of-the-art performance. For instance, on the YouTube-VIS-2019 dataset, TeViT achieves an average precision (AP) of 46.6 with an inference speed of 68.9 FPS, outperforming existing methods such as MaskProp and VisTR. TeViT consistently demonstrates improved metrics across YouTube-VIS-2021 and OVIS datasets as well, reflecting advancements in handling challenging real-world videos with significant temporal occlusion and deformation.

Implications and Future Prospects

TeViT’s ability to perform efficient temporal modeling proves critical for enhancing video instance segmentation, both practically and theoretically. On a practical level, the methods proposed by TeViT could improve the processing efficiencies of systems requiring extensive video data analyses, such as surveillance or multimedia content management. Theoretically, the integration of these temporal modeling mechanisms into vision transformers sets a precedent for future developments in video understanding tasks. By separating the frame and instance-level temporal modeling techniques, TeViT ensures a robust foundation for improving long-range dependency capture in video data, without dependency on extensive convolutional architectures.

Conclusion

In summary, the Temporally Efficient Vision Transformer framework delineated in this paper underscores the importance of efficient temporal context modeling. Through innovative mechanisms — specifically the messenger shift and spatiotemporal query interaction — TeViT not only transcends existing methodologies in video instance segmentation but also sheds light on potential advancements for transformer-based video understanding. Future explorations might involve refining these approaches to mitigate challenges posed by occlusion and motion deformation in more complex and lengthy videos.