Spectrum-guided Multi-granularity Referring Video Object Segmentation (2307.13537v1)

Abstract: Current referring video object segmentation (R-VOS) techniques extract conditional kernels from encoded (low-resolution) vision-language features to segment the decoded high-resolution features. We discovered that this causes significant feature drift, which the segmentation kernels struggle to perceive during the forward computation. This negatively affects the ability of segmentation kernels. To address the drift problem, we propose a Spectrum-guided Multi-granularity (SgMg) approach, which performs direct segmentation on the encoded features and employs visual details to further optimize the masks. In addition, we propose Spectrum-guided Cross-modal Fusion (SCF) to perform intra-frame global interactions in the spectral domain for effective multimodal representation. Finally, we extend SgMg to perform multi-object R-VOS, a new paradigm that enables simultaneous segmentation of multiple referred objects in a video. This not only makes R-VOS faster, but also more practical. Extensive experiments show that SgMg achieves state-of-the-art performance on four video benchmark datasets, outperforming the nearest competitor by 2.8% points on Ref-YouTube-VOS. Our extended SgMg enables multi-object R-VOS, runs about 3 times faster while maintaining satisfactory performance. Code is available at https://github.com/bo-miao/SgMg.

• The paper presents the SgMg framework, which overcomes feature drift by segmenting encoded features directly, leading to improved segmentation accuracy.
• It introduces Spectrum-guided Cross-modal Fusion (SCF) to enhance global context understanding between visual and linguistic modalities.
• It extends R-VOS to multi-object segmentation, achieving state-of-the-art performance with significant speed improvements and practical efficiency.

An Analysis of Spectrum-guided Multi-granularity Referring Video Object Segmentation

The paper introduces a novel approach to Referring Video Object Segmentation (R-VOS) by identifying and addressing a critical problem known as feature drift that affects the efficacy of current segmentation methods. R-VOS aims to accurately segment objects within a video sequence based on linguistic descriptions, posing significant challenges due to multi-modal reasoning requirements. The researchers propose an innovative method, titled Spectrum-guided Multi-granularity (SgMg) approach, which mitigates the feature drift issue and enhances segmentation accuracy through direct patch segmentation of encoded features and further optimization using visual details.

The traditional methodology in R-VOS involves decoding features to high-resolution before segmentation. This process, though beneficial for adding visual detail, results in feature drift, significantly impacting segmentation kernel performance. The authors argue that feature drift complicates segmentation as kernels, particularly Conditional Kernels, must adapt to varying features post-decoding, a challenge compounded by nonlinear processes and the inability to effectively perceive changes due to early prediction.

To overcome these issues, the SgMg approach follows a “segment-and-optimize” pipeline rather than the conventional “decode-and-segment” pathway. The segmentation is first applied directly to the encoded features to avoid the drift phenomena. The masks produced are refined using a Multi-granularity Segmentation Optimizer (MSO), which incorporates visual details to enhance the resolution and accuracy of the segmentation.

Furthermore, Spectrum-guided Cross-modal Fusion (SCF) is introduced to facilitate intra-frame global interactions in the spectral domain. This is grounded in the principle that operations in the spectral domain allow for efficient global context understanding, which proves beneficial for multimodal interactions necessary for precise video object segmentation. SCF leverages low-frequency spectrum components, which have been demonstrated to align well with semantic features, thereby amplifying the interaction process between visual and linguistic modalities.

Another significant contribution of the paper is the expansion of R-VOS capabilities through the introduction of a multi-object segmentation paradigm. This allows simultaneous segmentation of multiple referred objects within a video, enhancing computational efficiency and practicality in real-world applications. The extension builds on the foundation of SgMg, incorporating multi-instance fusion and decoupling strategies, facilitating feature sharing among object queries, thus speeding up processing without compromising performance.

The experimental results present compelling evidence of the efficacy of the proposed approach, with SgMg achieving state-of-the-art performance on prominent benchmark datasets such as Ref-YouTube-VOS, Ref-DAVIS17, A2D-Sentences, and JHMDB-Sentences. The remarkable result on Ref-YouTube-VOS, showcasing a 2.8% improvement over existing methodologies, underscores the effectiveness of addressing feature drift. Additionally, Fast SgMg demonstrates approximately a threefold increase in speed for multi-object R-VOS, presenting a promising avenue for practical deployment.

In conclusion, the proposed SgMg framework not only advances the field of R-VOS by addressing feature drift through direct encoded feature segmentation and incorporating multimodal fusion in the spectral domain but also sets a new precedent for efficient processing in scenarios requiring multi-object segmentation. The theoretical and practical implications, particularly the potential for adapting such frameworks in real-time applications, highlight a significant step forward in video object segmentation technologies. Future work may focus on further refining the SCF’s integration into broader multimodal systems and exploring the full potential of spectrum-guided operations across various domains in AI.