Collaborative Video Object Segmentation by Foreground-Background Integration (2003.08333v2)

Abstract: This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Different from previous practices that only explore the embedding learning using pixels from foreground object (s), we consider background should be equally treated and thus propose Collaborative video object segmentation by Foreground-Background Integration (CFBI) approach. Our CFBI implicitly imposes the feature embedding from the target foreground object and its corresponding background to be contrastive, promoting the segmentation results accordingly. With the feature embedding from both foreground and background, our CFBI performs the matching process between the reference and the predicted sequence from both pixel and instance levels, making the CFBI be robust to various object scales. We conduct extensive experiments on three popular benchmarks, i.e., DAVIS 2016, DAVIS 2017, and YouTube-VOS. Our CFBI achieves the performance (J$F) of 89.4%, 81.9%, and 81.4%, respectively, outperforming all the other state-of-the-art methods. Code: https://github.com/z-x-yang/CFBI.

• The paper introduces CFBI, a method that integrates foreground and background embeddings, enhancing video object segmentation accuracy across multiple benchmarks.
• It employs a dual embedding system—pixel-level and instance-level—combined with a Collaborative Ensembler using dilated convolutions and ASPP for effective feature aggregation.
• Experiments on DAVIS and YouTube-VOS datasets show that CFBI achieves state-of-the-art performance with competitive inference speed, highlighting its practical relevance.

An In-Depth Overview of CFBI: Collaborative Video Object Segmentation by Foreground-Background Integration

The paper "Collaborative Video Object Segmentation by Foreground-Background Integration" introduces CFBI, a compelling approach to semi-supervised video object segmentation (VOS) that capitalizes on foreground and background integration. The research focuses on enhancing embedding learning by equally emphasizing both foreground and background areas in videos, contrary to traditional methods that predominantly concentrate on foreground objects.

Key Contributions and Methodology

The CFBI framework introduces a novel way of embedding learning by treating the background with equal importance to the foreground. The integration of foreground and background embeddings is achieved through a collaborative approach, the core novelty of the paper, which aims to mitigate background confusion commonly encountered in video sequences featuring similar objects.

CFBI employs a two-tiered embedding system, encompassing pixel-level and instance-level embeddings. Pixel-level embedding enables the detailed matching of object features by leveraging both global and multi-local matching mechanisms, enhancing the robustness against varying object movements across frames. Instance-level embedding complements this by utilizing an attention mechanism to assist in segmenting larger objects, thereby overcoming the limitations of pixel-level embeddings in handling large-scale features.

A crucial aspect of CFBI is the Collaborative Ensembler (CE), which effectively aggregates embedded features across multiple levels, thereby enabling the model to maintain a simplistic yet highly effective architecture. The CE incorporates dilated convolutions and an Atrous Spatial Pyramid Pooling (ASPP) module for improving feature context aggregation.

Experimental Evaluation

CFBI demonstrates impressive performance across key benchmarks, achieving notable results on DAVIS 2016, DAVIS 2017, and YouTube-VOS datasets. It achieves $\mathcal{J}{content}\mathcal{F}$ scores of 89.4%, 81.9%, and 81.4%, respectively. These scores surpass those achieved by previous state-of-the-art methods without resorting to extensive simulated data or fine-tuning at the testing phase. This efficiency is achieved while maintaining a considerable inference speed of approximately 5 FPS, highlighting the method's practicality in applications necessitating real-time processing.

Additional techniques such as multi-scale and flip augmentation further enhance CFBI performance, demonstrating the method's robustness and adaptability to various experimental conditions.

Implications and Prospects for Future Work

CFBI underscores the importance of treating background characteristics equivalently to foreground features, drawing attention to potential improvements for related tasks such as video instance segmentation and interactive video object segmentation. By integrating robust foreground and background embeddings, the CFBI framework sets a new standard for embedding learning mechanisms in VOS.

Future research could build on this foundation by exploring more advanced attention mechanisms or integrating reinforcement learning techniques to dynamically adjust embedding strategies across varying video contexts. Additionally, CFBI's approach can inspire developments beyond VOS, such as in autonomous driving systems and augmented reality applications, where understanding the interplay between moving objects and their surrounding contexts is critical.

In conclusion, CFBI represents a notable advancement in the field of computer vision, providing a comprehensive and robust framework for video object segmentation that effectively balances complexity with performance.