Towards Robust Video Object Segmentation with Adaptive Object Calibration (2207.00887v1)

Abstract: In the booming video era, video segmentation attracts increasing research attention in the multimedia community. Semi-supervised video object segmentation (VOS) aims at segmenting objects in all target frames of a video, given annotated object masks of reference frames. Most existing methods build pixel-wise reference-target correlations and then perform pixel-wise tracking to obtain target masks. Due to neglecting object-level cues, pixel-level approaches make the tracking vulnerable to perturbations, and even indiscriminate among similar objects. Towards robust VOS, the key insight is to calibrate the representation and mask of each specific object to be expressive and discriminative. Accordingly, we propose a new deep network, which can adaptively construct object representations and calibrate object masks to achieve stronger robustness. First, we construct the object representations by applying an adaptive object proxy (AOP) aggregation method, where the proxies represent arbitrary-shaped segments at multi-levels for reference. Then, prototype masks are initially generated from the reference-target correlations based on AOP. Afterwards, such proto-masks are further calibrated through network modulation, conditioning on the object proxy representations. We consolidate this conditional mask calibration process in a progressive manner, where the object representations and proto-masks evolve to be discriminative iteratively. Extensive experiments are conducted on the standard VOS benchmarks, YouTube-VOS-18/19 and DAVIS-17. Our model achieves the state-of-the-art performance among existing published works, and also exhibits superior robustness against perturbations. Our project repo is at https://github.com/JerryX1110/Robust-Video-Object-Segmentation

Overview of Adaptive Object Calibration for Video Object Segmentation

This paper introduces an innovative approach to enhancing the robustness of semi-supervised Video Object Segmentation (VOS) through an adaptive object calibration strategy. The authors propose a deep neural network architecture that leverages two main components: Adaptive Object Proxy (AOP) representation and discriminative object mask calibration, aimed at improving segmentation accuracy and resilience against perturbations commonly found in video sequences.

The conventional methods in VOS, which primarily rely on pixel-wise tracking mechanisms, are deemed insufficient due to their vulnerability to noise and perturbations, particularly in challenging scenes with multiple similar objects. The proposed method addresses these limitations by focusing on robust object-level representation and calibrated mask generation.

Adaptive Object Proxy Representation

A significant contribution of this work is the introduction of an Adaptive Object Proxy (AOP) aggregation method to construct robust object representations. This method involves the multi-level clustering of pixel-level features to create semantic proxies that accurately encapsulate object-specific information. Such proxies mitigate the weaknesses associated with direct pixel-to-pixel matching by aggregating features of similar semantics, thus enhancing matching robustness and reducing noise susceptibility.

The AOP representation ensures that object proxies accurately represent the underlying shape and semantic details across frames, facilitating more reliable correlation computation between reference and target frames.

Discriminative Object Mask Calibration

The proposed network further incorporates a discriminative object calibration mechanism, which iteratively refines the initial mask estimates derived from the correlation-based proto-maps. This calibration involves network modulation driven by condition codes that are progressively adjusted to enhance feature discrimination between target and non-target objects.

The conditional decoder integrated into this system performs multi-object discrimination by factoring in interactions across different object representations. This results in highly discriminative mask refinements that successfully distinguish target objects even in adverse conditions, such as overlap or occlusion.

Experimental Evaluation

The experimental results reported in the paper demonstrate the efficacy of the proposed method, achieving state-of-the-art performance on standard VOS benchmarks such as YouTube-VOS and DAVIS datasets. Notably, the method showcases robust performance under various perturbation scenarios—ranging from noise injection to image blurring—exceeding the capabilities of several competitive baseline methods.

Quantitatively, significant gains in the metrics, particularly in unseen object categories, underscore the model's generalization ability. Furthermore, the robustness evaluation using perturbed datasets highlights the model's effectiveness in minimizing performance degradation due to input perturbations.

Practical Implications and Future Directions

The advancements presented in this research have substantial implications for real-world video analytics applications, especially in domains where visual quality and construct variability pose challenges, such as autonomous driving and surveillance.

Future work in this area could explore more adaptive and dynamic strategies for proxy initialization and cluster formation, as well as extending the robustness analysis to encompass a broader range of natural and synthetic perturbations. Additionally, integrating this framework with other deep learning paradigms, such as those involving temporal attention mechanisms or unsupervised learning, could yield further improvements in VOS accuracy and operational resilience.

In conclusion, this paper presents a novel methodological leap in VOS through adaptive calibration techniques, offering significant enhancements in robustness and performance over conventional methods. The dual focus on representation and calibration provides a foundational approach that future research can build upon to address the continuing challenges inherent in video object segmentation.