Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation

2025-07-08

AI Summary

Video instance segmentation (VIS) suffers from limited robustness under occlusion, motion blur, and appearance variations. This paper pioneers a systematic investigation into monocular depth estimation as a geometric cue to enhance VIS performance, proposing three geometry-aware fusion paradigms: Depth-channel Expansion (EDC), Shared Vision Transformer (ViT) Backbone (SV), and Depth Supervision (DS). These methods improve temporal association and representation learning via multimodal feature alignment, operating seamlessly on transformer-based backbones (e.g., Swin-L) without requiring additional annotations. Evaluated on the OVIS benchmark, EDC achieves 56.2 AP—setting a new state-of-the-art at the time—and significantly outperforms prior approaches. The results empirically validate the effectiveness and generalizability of geometric priors in complex video scenes, advancing VIS toward multimodal, geometry-aware modeling.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Video Instance Segmentation (VIS) fundamentally struggles with pervasive challenges including object occlusions, motion blur, and appearance variations during temporal association. To overcome these limitations, this work introduces geometric awareness to enhance VIS robustness by strategically leveraging monocular depth estimation. We systematically investigate three distinct integration paradigms. Expanding Depth Channel (EDC) method concatenates the depth map as input channel to segmentation networks; Sharing ViT (SV) designs a uniform ViT backbone, shared between depth estimation and segmentation branches; Depth Supervision (DS) makes use of depth prediction as an auxiliary training guide for feature learning. Though DS exhibits limited effectiveness, benchmark evaluations demonstrate that EDC and SV significantly enhance the robustness of VIS. When with Swin-L backbone, our EDC method gets 56.2 AP, which sets a new state-of-the-art result on OVIS benchmark. This work conclusively establishes depth cues as critical enablers for robust video understanding.

Problem

• Enhance Video Instance Segmentation robustness using geometric cues
• Address object occlusions, motion blur, appearance variations in VIS
• Integrate monocular depth estimation to improve temporal association

Innovation

• Leverages monocular depth estimation for robustness
• Expands depth channel in segmentation networks
• Shares ViT backbone between depth and segmentation