Video Instance Segmentation with a Propose-Reduce Paradigm (2103.13746v2)

Abstract: Video instance segmentation (VIS) aims to segment and associate all instances of predefined classes for each frame in videos. Prior methods usually obtain segmentation for a frame or clip first, and merge the incomplete results by tracking or matching. These methods may cause error accumulation in the merging step. Contrarily, we propose a new paradigm -- Propose-Reduce, to generate complete sequences for input videos by a single step. We further build a sequence propagation head on the existing image-level instance segmentation network for long-term propagation. To ensure robustness and high recall of our proposed framework, multiple sequences are proposed where redundant sequences of the same instance are reduced. We achieve state-of-the-art performance on two representative benchmark datasets -- we obtain 47.6% in terms of AP on YouTube-VIS validation set and 70.4% for J&F on DAVIS-UVOS validation set. Code is available at https://github.com/dvlab-research/ProposeReduce.

Video Instance Segmentation with a Propose-Reduce Paradigm

The paper "Video Instance Segmentation with a Propose-Reduce Paradigm" introduces a novel approach to the problem of video instance segmentation (VIS). This task involves the simultaneous classification, segmentation, and tracking of object instances across video frames—a complex challenge in the domain of video understanding.

Key Contributions

The authors of this paper propose a paradigm termed Propose-Reduce, which fundamentally alters the approach to VIS by avoiding sequential errors typically accumulated from tracking or matching steps in previous methodologies. The Propose-Reduce paradigm consists of two stages:

1. Sequence Proposal Generation: Unlike conventional techniques which rely on two-step processes—detecting instances in frames followed by frame-by-frame tracking—the Propose-Reduce paradigm generates sequence proposals based on multiple key frames. This approach ensures robust, high-recall instance detection by allowing for long-term propagation throughout the video, effectively mitigating the potential for error accumulation.
2. Sequence Proposal Reduction: It introduces a reduction phase where redundant sequence proposals related to the same instances are minimized. This phase is inspired by non-maximum suppression (NMS) techniques used in static image instance segmentation. By extending NMS to work at the sequence level, it efficiently filters out duplicate proposals based on calculated sequence scores and intersection-over-union metrics.

Performance Metrics

The research exhibits compelling results on benchmark datasets. The Propose-Reduce paradigm achieves state-of-the-art performance with a mean Average Precision (AP) of 47.6% on the YouTube-VIS validation set and a J&F score of 70.4% on the DAVIS-UVOS validation set. These results underscore the paradigm's efficacy in producing high-quality VIS outputs without necessitating additional post-processing refinement steps.

Theoretical and Practical Implications

The Propose-Reduce paradigm validates its effectiveness by mitigating error accumulation, a long-standing challenge in VIS tasks affected by object occlusion and rapid motion. Practically, the approach enhances real-world applications, such as autonomous driving and advanced video editing, where consistent and accurate object tracking is crucial.

Speculation on Future Developments

From a theoretical perspective, this paradigm suggests promising directions for integrating video-based propagation techniques into broader AI models. Future research could explore improved methods for selecting key frames dynamically based on video content or investigate more sophisticated reduction algorithms that consider semantic relationships and motion predictions.

In conclusion, the Propose-Reduce paradigm presents a refined and innovative methodology for addressing the video instance segmentation task, offering both efficient computation and enhanced accuracy without incurring the complexities and accumulative errors of traditional tracking-based approaches.