DVIS: Decoupled Video Instance Segmentation Framework (2306.03413v3)

Abstract: Video instance segmentation (VIS) is a critical task with diverse applications, including autonomous driving and video editing. Existing methods often underperform on complex and long videos in real world, primarily due to two factors. Firstly, offline methods are limited by the tightly-coupled modeling paradigm, which treats all frames equally and disregards the interdependencies between adjacent frames. Consequently, this leads to the introduction of excessive noise during long-term temporal alignment. Secondly, online methods suffer from inadequate utilization of temporal information. To tackle these challenges, we propose a decoupling strategy for VIS by dividing it into three independent sub-tasks: segmentation, tracking, and refinement. The efficacy of the decoupling strategy relies on two crucial elements: 1) attaining precise long-term alignment outcomes via frame-by-frame association during tracking, and 2) the effective utilization of temporal information predicated on the aforementioned accurate alignment outcomes during refinement. We introduce a novel referring tracker and temporal refiner to construct the \textbf{D}ecoupled \textbf{VIS} framework (\textbf{DVIS}). DVIS achieves new SOTA performance in both VIS and VPS, surpassing the current SOTA methods by 7.3 AP and 9.6 VPQ on the OVIS and VIPSeg datasets, which are the most challenging and realistic benchmarks. Moreover, thanks to the decoupling strategy, the referring tracker and temporal refiner are super light-weight (only 1.69\% of the segmenter FLOPs), allowing for efficient training and inference on a single GPU with 11G memory. The code is available at \href{https://github.com/zhang-tao-whu/DVIS}{https://github.com/zhang-tao-whu/DVIS}.

DVIS: Decoupled Video Instance Segmentation Framework

Video Instance Segmentation (VIS) is an essential task in computer vision with widespread applications, including autonomous driving and video editing. The task involves simultaneously identifying, segmenting, and tracking instances across video frames. Current methods, however, exhibit limitations when dealing with complex and lengthy real-world videos. These limitations often stem from a coupled modeling paradigm that either inefficiently handles long-term spatio-temporal dependencies or underutilizes temporal information. The paper "DVIS: Decoupled Video Instance Segmentation Framework" introduces a novel framework designed to address these challenges through a decoupling strategy that separates the VIS task into three independent sub-tasks: segmentation, tracking, and refinement.

Contributions

The primary contribution of DVIS is its decoupled approach, which consists of three independent components: a segmenter, a referring tracker, and a temporal refiner. This architectural innovation allows DVIS to achieve superior performance on complex videos by effectively utilizing both frame-by-frame alignment for instance tracking and long-term temporal refinement for accuracy optimization.

Key highlights include:

1. Referring Tracker: The core concept of the referring tracker is to model inter-frame associations as a denoising task. This is achieved using a novel Referring Cross Attention (RCA) module that enhances tracking robustness. The RCA operates by leveraging the instance representations from contiguous frames while avoiding their blend, thus preserving instance identity across frames. This design choice effectively mitigates issues associated with high occlusion or significant instance transformations over time.
2. Temporal Refiner: The temporal refiner is designed to leverage the complete temporal context of a video, refining both segmentation and tracking outcomes. It utilizes a combination of temporal convolution and self-attention mechanisms, alongside cross-attention from multiple frames, to correct and integrate temporal information across an entire video.
3. Resource Efficiency: Notably, the referring tracker and temporal refiner are computationally lightweight, using only a fraction (1.69% of segmenter FLOPs) of the computational resources required by the segmenter. This efficiency allows for training and inference on cost-effective hardware setups, such as a single GPU with limited memory.

Results

The empirical results reported in the paper demonstrate that DVIS achieves state-of-the-art performance across widely recognized benchmarks: OVIS, YouTube-VIS 2019-2022, and VIPSeg datasets. Specifically, DVIS surpasses existing state-of-the-art by 7.3 Average Precision (AP) and 9.6 Video Panoptic Quality (VPQ) on the OVIS and VIPSeg benchmarks, respectively. Such enhancements are significant indicators of the framework's ability to handle challenging scenarios involving occlusion and complex motion paths effectively.

Implications and Future Directions

Practically, the DVIS framework's decoupled nature allows for modular improvements in each sub-task without affecting others, introducing flexibility in algorithmic design that can be adapted to various segmentation challenges. Theoretically, this approach may inspire further exploration into task-specific decoupling in computer vision, potentially leading to more robust and efficient models in other segmentation-related tasks.

A promising direction could involve extending DVIS to operate directly on streaming video data, where real-time updates and infinite video lengths present additional challenges. Additionally, implementing specific mechanisms for dynamically handling instances that appear and disappear can enhance DVIS’s applicability to real-world videos, a critical consideration emphasized by the researchers.

Overall, DVIS represents a significant advancement in video instance segmentation methodology, with its innovative decoupling strategy setting a new benchmark for performance and efficiency in both online and offline settings. This framework has the potential to significantly impact the broader landscape of video analysis and processing.