Unified Video Instance and Panoptic Segmentation with Context-Aware Object Queries

Unified Video Instance and Panoptic Segmentation with Context-Aware Object Queries

Table of Contents

  1. Introduction
  2. Background
  3. Challenges in Video Segmentation
  4. Context-Aware Relative Object Queries
  5. Propagation of Object Queries
  6. Relative Positional Encodings
  7. Performance Evaluation
  8. Comparison with Existing Methods
  9. Identity Switches and Resolving with Relative Positional Encodings
  10. Conclusion

Introduction

Video segmentation is an important task in computer vision that involves segmenting and associating objects of interest in a video. Different subtasks are defined Based on the specific objects being targeted, such as video instance segmentation, multi-object tracking and segmentation, and video panoptic segmentation. Recent advancements in transformer-based architectures have shown promising results for image-level segmentation using object queries. However, using these architectures for video segmentation poses two main challenges: producing temporally consistent object queries that account for appearance and position changes of objects across frames, and seamlessly propagating object queries while keeping the frame processing online.

Background

Transformers have recently been applied to video segmentation tasks as an extension to their success in image segmentation. Some methods rely on local image-level object queries or global video-level object queries to process video frames independently. Local object queries can be merged using association steps or query vector propagation, while global object queries process the entire video at once. However, global object queries struggle to Scale to long videos without heuristic clip merging. Surprisingly, global object queries do not necessarily result in more accurate representations of objects spatiotemporally compared to local object queries.

Challenges in Video Segmentation

The best methods in video instance segmentation, such as MinVIS, generate query vectors independently frame-by-frame. This raises the question of why global object queries fail to accurately represent objects spatiotemporally. Upon closer examination, it is observed that object queries often rely too heavily on the static Spatial positions of objects in a few frames, leading to poor encoding of position changes. Additionally, it remains unclear how queries can be extended seamlessly across frames while maintaining sequential frame processing.

Context-Aware Relative Object Queries

To address the challenges Mentioned above, this work introduces "Context-Aware Relative Object Queries" for video segmentation. These object queries continuously and seamlessly propagate across frames without the need for post-processing. Relative position encodings are used to capture position changes of objects in motion more effectively compared to absolute position encodings used in prior works. The proposed approach matches or surpasses the state-of-the-art results in video instance segmentation, multi-object tracking and segmentation, and video panoptic segmentation.

Propagation of Object Queries

In the proposed approach, object queries represent all objects of interest in a video without introducing new object queries in each frame. Queries are activated if the objects they represent appear in a frame, and they are either expressed or nascent. Object queries obtained in the first frame are propagated to the next frame iteratively. For frame tau_t, object queries from the previous frame (q_tau_t-1) are propagated and modulated. By leveraging the gradual changes in video frames, Meaningful information from q_tau_t-1 enhances the representation of objects in frame tau_t. Skipping the early layers ensures that L number of transformer decoder layers is not necessary to modulate the object queries for the Current frame.

Relative Positional Encodings

Previous works employ absolute positional encodings in the transformer decoder. This involves a cross Attention operation with an absolute cross attention matrix. However, it has been experimentally observed that these absolute positional encodings introduce undesired dependence of object queries on spatial positions in images. In this work, relative positional encodings are introduced, which use a relative cross attention matrix. Relative positions, denoted as xi_rel, are learned in a manner proposed by Dai et al. for language modeling. The use of relative positional encodings significantly improves the performance of the approach compared to absolute ones.

Performance Evaluation

The proposed approach is evaluated on video instance segmentation, multi-object tracking and segmentation, and video panoptic segmentation tasks. It surpasses MinVIS on the OVIS dataset for video instance segmentation. In multi-object tracking and segmentation, it achieves the best results even when compared to highly specialized methods like PointTrack. For video panoptic segmentation, the approach performs similarly or better than specialized 2-branch architectures like Vip-DeepLab and VPS-Net. The use of relative positions instead of absolute ones improves association accuracy, detection accuracy, and the overall accuracy (HOTA) of the approach.

Comparison with Existing Methods

When comparing the proposed approach with existing methods, it is observed that relative positional encodings successfully resolve identity switches that occur with absolute positional encodings. These identity switches are especially prominent in scenes with drastic viewpoint changes or long-term occlusions. The proposed method retains the identities of objects in such challenging scenarios, ensuring consistency in object segmentation.

Conclusion

In conclusion, this work introduces context-aware relative object queries for video segmentation, addressing the challenges of producing temporally consistent object queries and seamlessly propagating them across frames. The use of relative positional encodings enhances the representation of position changes in objects. The proposed approach achieves state-of-the-art or surpasses existing methods in video instance segmentation, multi-object tracking and segmentation, and video panoptic segmentation tasks.

Please visit our poster on Tuesday afternoon for further details. Thank You very much!


Highlights

  • Introduction to context-aware relative object queries for video segmentation
  • Challenges in producing temporally consistent object queries and seamlessly propagating them across frames
  • Use of relative positional encodings to better capture position changes of objects
  • Performance evaluation on video instance segmentation, multi-object tracking and segmentation, and video panoptic segmentation tasks
  • Comparison with existing methods, highlighting the AdVantage of relative positional encodings
  • Achievement of state-of-the-art results or surpassing existing methods in video segmentation tasks

FAQ

Q: What is video segmentation? A: Video segmentation is the task of segmenting and associating objects of interest in a video. It involves identifying and tracking objects across frames.

Q: What are the challenges in video segmentation? A: The challenges in video segmentation include producing temporally consistent object queries that account for appearance and position changes of objects, and seamlessly propagating object queries while keeping the frame processing online.

Q: How does the proposed approach address these challenges? A: The proposed approach introduces context-aware relative object queries that continuously propagate across frames without requiring post-processing. Relative positional encodings are used to better capture position changes of objects.

Q: How does the performance of the proposed approach compare to existing methods? A: The proposed approach achieves state-of-the-art or surpasses existing methods in video instance segmentation, multi-object tracking and segmentation, and video panoptic segmentation tasks.

Q: What is the advantage of using relative positional encodings? A: Relative positional encodings resolve identity switches that occur with absolute positional encodings, ensuring consistency in object segmentation even in challenging scenarios.


Resources:

Most people like

Find AI tools in Toolify

Join TOOLIFY to find the ai tools

Get started

Sign Up
App rating
4.9
AI Tools
20k+
Trusted Users
5000+
No complicated
No difficulty
Free forever
Browse More Content