MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training (2208.02245v1)

Abstract: We propose MinVIS, a minimal video instance segmentation (VIS) framework that achieves state-of-the-art VIS performance with neither video-based architectures nor training procedures. By only training a query-based image instance segmentation model, MinVIS outperforms the previous best result on the challenging Occluded VIS dataset by over 10% AP. Since MinVIS treats frames in training videos as independent images, we can drastically sub-sample the annotated frames in training videos without any modifications. With only 1% of labeled frames, MinVIS outperforms or is comparable to fully-supervised state-of-the-art approaches on YouTube-VIS 2019/2021. Our key observation is that queries trained to be discriminative between intra-frame object instances are temporally consistent and can be used to track instances without any manually designed heuristics. MinVIS thus has the following inference pipeline: we first apply the trained query-based image instance segmentation to video frames independently. The segmented instances are then tracked by bipartite matching of the corresponding queries. This inference is done in an online fashion and does not need to process the whole video at once. MinVIS thus has the practical advantages of reducing both the labeling costs and the memory requirements, while not sacrificing the VIS performance. Code is available at: https://github.com/NVlabs/MinVIS

Analyzing the MinVIS Framework for Video Instance Segmentation

The paper "MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training" introduces an innovative approach, termed MinVIS, that challenges traditional methodologies in video instance segmentation (VIS). It proposes an efficient, image-based framework that eschews conventional video-based training paradigms while achieving state-of-the-art performance.

Overview and Methodology

At the core of MinVIS is a query-based image instance segmentation model that processes frames independently, treating each frame as a distinct image. This is a significant departure from traditional VIS frameworks which typically employ a per-clip approach, processing spatial-temporal volumes in a video to predict object instance masks. MinVIS, on the other hand, eliminates the necessity for complex video-based architectures and training by decoupling temporal association from instance segmentation.

During inference, MinVIS independently applies the trained image segmentation model to video frames. The temporal coherence, required for tracking object instances across frames, is achieved through bipartite matching of query embeddings, a task traditionally addressed using computationally expensive and memory-intensive heuristics. This paradigm suggests that discriminative queries, once trained for intra-frame instance differentiation, inherently possess temporal consistency, enabling effective instance tracking without direct video-based training.

Empirical Results and Implications

MinVIS demonstrates compelling empirical results, notably improving performance on datasets like the Occluded VIS (OVIS) by over 10% average precision (AP) compared to existing methods. The framework also shows resilience in scenarios requiring significant sub-sampling of annotated frames, maintaining competitive performance even with as little as 1% of labeled data on benchmarks such as YouTube-VIS 2019 and 2021. These results underscore the potential of MinVIS to reduce labeling costs and computational overhead significantly, without sacrificing accuracy.

The results on the OVIS dataset are particularly noteworthy. The challenge of tracking through heavy occlusions was met with a 13% AP improvement over the framework’s per-clip counterpart, which illustrates MinVIS’s robustness in handling occlusions—a typical bottleneck for methods relying on spatiotemporal coherence achieved through complex post-processing heuristics.

Theoretical and Practical Impacts

The innovation of MinVIS lies in its ability to utilize an image-based model to achieve temporal consistency, suggesting a reevaluation of established methodologies that emphasize heavy temporal modeling and full-frame video processing. This approach not only questions the necessity of complex architectures but also highlights the untapped potential of individual frame segmentation models enhanced with modality-specific query embeddings for temporal tasks.

Practical advantages are evident in reduced training complexity, lower memory requirements, and significantly fewer labeled data needs, thereby elevating MinVIS as a feasible solution for real-world applications requiring scalable and efficient video processing.

Future Directions

Future research directions could explore semi-supervised or few-shot learning paradigms that leverage MinVIS's strengths, further reducing labeled data requirements by exploiting unlabeled video segments. Additionally, integrating video-based information without escalating complexity, potentially via unsupervised or self-supervised methods, could enhance query embeddings' temporal robustness.

While MinVIS demonstrates substantial promise, there remains room to refine the theoretical underpinnings that enable independent image queries to generalize effectively across frames, particularly under intricate occlusion patterns. Understanding these dynamics could lead to enhanced architectures that seamlessly integrate image and video paradigms for optimal VIS performance.

In summary, MinVIS represents a pivotal step forward in VIS by challenging traditional notions of video-based training necessity and offering a streamlined, effective alternative that aligns with the evolving needs of scalable, resource-efficient AI systems.