A Generalized Framework for Video Instance Segmentation (2211.08834v2)

Abstract: The handling of long videos with complex and occluded sequences has recently emerged as a new challenge in the video instance segmentation (VIS) community. However, existing methods have limitations in addressing this challenge. We argue that the biggest bottleneck in current approaches is the discrepancy between training and inference. To effectively bridge this gap, we propose a Generalized framework for VIS, namely GenVIS, that achieves state-of-the-art performance on challenging benchmarks without designing complicated architectures or requiring extra post-processing. The key contribution of GenVIS is the learning strategy, which includes a query-based training pipeline for sequential learning with a novel target label assignment. Additionally, we introduce a memory that effectively acquires information from previous states. Thanks to the new perspective, which focuses on building relationships between separate frames or clips, GenVIS can be flexibly executed in both online and semi-online manner. We evaluate our approach on popular VIS benchmarks, achieving state-of-the-art results on YouTube-VIS 2019/2021/2022 and Occluded VIS (OVIS). Notably, we greatly outperform the state-of-the-art on the long VIS benchmark (OVIS), improving 5.6 AP with ResNet-50 backbone. Code is available at https://github.com/miranheo/GenVIS.

A Generalized Framework for Video Instance Segmentation

The paper introduces GenVIS, a generalized framework for Video Instance Segmentation (VIS), addressing recent challenges faced by this field, particularly the segmentation of long videos with complex and occluded sequences. This paper highlights that existing VIS methods are hindered by a training-inference discrepancy. GenVIS is proposed as a solution without the need for intricate architectures or additional post-processing, achieving state-of-the-art results.

Key Contributions

1. Learning Strategy and Target Label Assignment: GenVIS emphasizes a query-based training pipeline that integrates a novel target label assignment, Unified Video Label Assignment (UVLA). This approach ensures seamless integration of multiple clips during training, efficiently bridging the gap between training and inference scenarios for long video analysis.
2. Memory Mechanism: A notable component of GenVIS is its memory mechanism, which allows the framework to incorporate prior knowledge from previously processed video states. This technique enhances the model's capability to handle scenarios typical of extended video sequences.
3. Flexible Execution Modes: By focusing on relationships between separate frames or clips, GenVIS can operate flexibly in both online and semi-online modes. This adaptability is advantageous for processing real-world videos with variable lengths.

Performance Evaluation

GenVIS exhibits exemplary performance across several prominent VIS benchmarks, including YouTube-VIS 2019/2021/2022 and Occluded VIS (OVIS). Particularly, it surpasses previous state-of-the-art methods on the OVIS dataset, improving by 5.6 AP using a ResNet-50 backbone.

Implications and Future Directions

The contributions of GenVIS have significant implications for both practical applications and theoretical advancements in VIS. Practically, it allows for more robust video content analysis, essential for applications in surveillance, autonomous navigation, and multimedia retrieval. Theoretically, it challenges existing paradigms in VIS, promoting strategies that address the training-inference gap more effectively.

Future developments could explore extending similar training strategies and memory integrations to other temporal video tasks, such as action recognition or behavior analysis. Further research may also delve into enhancing computational efficiency without sacrificing segmenting accuracy, encouraging broader applicability in resource-constrained environments.

In conclusion, GenVIS presents a compelling case for revisiting how VIS systems are trained and deployed, emphasizing the importance of aligning these processes to better cater to the demands of real-world video complexity. This approach not only advances the state-of-the-art in video segmentation but also sets the stage for future research to build upon these novel training and inference methodologies.