STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos (2003.08429v4)

Abstract: Existing methods for instance segmentation in videos typically involve multi-stage pipelines that follow the tracking-by-detection paradigm and model a video clip as a sequence of images. Multiple networks are used to detect objects in individual frames, and then associate these detections over time. Hence, these methods are often non-end-to-end trainable and highly tailored to specific tasks. In this paper, we propose a different approach that is well-suited to a variety of tasks involving instance segmentation in videos. In particular, we model a video clip as a single 3D spatio-temporal volume, and propose a novel approach that segments and tracks instances across space and time in a single stage. Our problem formulation is centered around the idea of spatio-temporal embeddings which are trained to cluster pixels belonging to a specific object instance over an entire video clip. To this end, we introduce (i) novel mixing functions that enhance the feature representation of spatio-temporal embeddings, and (ii) a single-stage, proposal-free network that can reason about temporal context. Our network is trained end-to-end to learn spatio-temporal embeddings as well as parameters required to cluster these embeddings, thus simplifying inference. Our method achieves state-of-the-art results across multiple datasets and tasks. Code and models are available at https://github.com/sabarim/STEm-Seg.

• The paper introduces a proposal-free architecture that segments and tracks video objects in a single processing stage.
• It employs spatio-temporal embeddings and a Temporal Squeeze-Expand Decoder to efficiently cluster pixels by instance.
• STEm-Seg achieves state-of-the-art results on benchmarks like DAVIS-19 and YouTube-VIS, demonstrating improved accuracy and reduced ID switches.

STEm-Seg: A Unified Approach for Spatio-Temporal Instance Segmentation in Videos

The paper presents a novel method called STEm-Seg, designed for instance segmentation and tracking in videos through the modeling of video clips as 3D spatio-temporal volumes. The central innovation lies in treating the entire video clip as a single space-time continuum, which allows the method to segment and track instances in a single processing stage, bypassing the traditional multi-stage, detection followed by tracking paradigm.

Methodology

STEm-Seg introduces spatio-temporal embeddings that are capable of clustering pixels associated with specific object instances throughout a video. Instead of associating detections over frames, as done in conventional methods, the approach optimizes the embeddings to directly infer object associations. To enhance its capability, it employs novel mixing functions that enrich the feature representations needed for embedding space clustering.

Key components of the approach include:

1. Proposal-Free Architecture: The network operates without the need for proposal generation. This significantly reduces computational overhead and complexity, enabling end-to-end training that encapsulates both spatial and temporal dynamics in the video.
2. Learning Spatio-Temporal Embeddings: The embeddings are dimensionally structured to capture spatial coordinates and include additional "free" dimensions. These dimensions afford the network extra degrees of freedom, improving instance separation quality.
3. Clustering via Learned Parameters: The method trains embeddings in a category-agnostic manner, such that pixels of the same object instance are grouped into a Gaussian distribution in embedding space. The model learns optimal clustering parameters directly.
4. Temporal Squeeze-Expand Decoder (TSE): A new network decoder design that compresses and then reconstructs temporal information, allowing the incorporation of dynamic temporal context to strip out the need for separate temporal association mechanics.

Experimental Results and Implications

STEm-Seg achieves state-of-the-art performance on diverse benchmarks, namely DAVIS 2019 (Unsupervised Video Object Segmentation) and YouTube-VIS (Video Instance Segmentation), demonstrating versatility across contexts involving multiple object classes (KITTI-MOTS dataset).

Numerical results indicate that STEm-Seg improves upon existing solutions by integrating efficient embeddings with strong temporal reasoning:

• On DAVIS-19's unsupervised track, STEm-Seg achieves a mean $\mathcal{J}\text{content}\mathcal{F}$ score of 64.7%, outperforming leading methods that employ complex post-processing or multi-network setups.
• On YouTube-VIS, the method registers a top $\mathcal{AP}$ of 34.6, showing significant improvement over the best competing model.
• The system yields higher precision temporal associations on KITTI-MOTS, notable through fewer ID switches compared to TrackR-CNN.

This research provides significant insights into effective single-stage segmentation and tracking in video contexts. The streamlined architecture exemplified by STEm-Seg demonstrates how integrated spatio-temporal modeling can supplant traditional detection-plus-tracking paradigms, offering a high-performance alternative with robust inference.

Future Directions

The approach lays ground for future exploration into more adaptive and generalized spatio-temporal modeling frameworks. Potential advancements may lie in optimizing free-embedding dimensions dynamically in real-time scenarios or extending the network's capacity for semantic inference across significantly larger and more unpredictable datasets.

Additionally, integrating such methodologies in autonomous systems and robotics, where online processing efficiency is paramount, could yield practical advantages in real-world deployments. Further paper into energy-efficient implementations of 3D spatio-temporal approaches is also recommended for broader applicability in mobile and edge computing environments.