Hierarchical Memory Matching Network for Video Object Segmentation (2109.11404v1)

Abstract: We present Hierarchical Memory Matching Network (HMMN) for semi-supervised video object segmentation. Based on a recent memory-based method [33], we propose two advanced memory read modules that enable us to perform memory reading in multiple scales while exploiting temporal smoothness. We first propose a kernel guided memory matching module that replaces the non-local dense memory read, commonly adopted in previous memory-based methods. The module imposes the temporal smoothness constraint in the memory read, leading to accurate memory retrieval. More importantly, we introduce a hierarchical memory matching scheme and propose a top-k guided memory matching module in which memory read on a fine-scale is guided by that on a coarse-scale. With the module, we perform memory read in multiple scales efficiently and leverage both high-level semantic and low-level fine-grained memory features to predict detailed object masks. Our network achieves state-of-the-art performance on the validation sets of DAVIS 2016/2017 (90.8% and 84.7%) and YouTube-VOS 2018/2019 (82.6% and 82.5%), and test-dev set of DAVIS 2017 (78.6%). The source code and model are available online: https://github.com/Hongje/HMMN.

Hierarchical Memory Matching Network for Video Object Segmentation

The paper "Hierarchical Memory Matching Network for Video Object Segmentation" introduces a novel approach for addressing the task of video object segmentation through the utilization of a Hierarchical Memory Matching Network (HMMN). This network leverages both spatial and temporal information effectively and is benchmarked on prominent datasets, including DAVIS 2016, DAVIS 2017, and YouTube-VOS.

Network Architecture and Implementation

The HMMN architecture is elaborated upon with significant focus on its unique components. The network incorporates a hierarchical approach to memory matching, with particular emphasis on top-$k$ guided memory matching modules situated at different stages of the convolutional backbone. The reduction of $k$ at res2 to $k/4$ optimizes computational efficiency while maintaining accuracy. The neural architecture also includes a decoder inspired by STM (Space-Time Memory Networks) but introduces value embedding layers instead of conventional convolutions in refinement modules, which facilitates improved feature encoding.

Quantitative Results

The HMMN achieves competitive performance across several video object segmentation benchmarks, including surpassing numerous state-of-the-art approaches. In particular, the results on the DAVIS 2016 validation set show improved performance over methods that utilize additional YouTube-VOS data, such as KMN (+YV) and CFBI (+YV), with the HMMN achieving $\mathcal{J} = 89.6$ and $\mathcal{F} = 92.0$. Similarly, strong results are observed on DAVIS 2017 validation and test-dev sets as well as YouTube-VOS validation set, with HMMN (+YV) achieving $\mathcal{J{content}F} = 78.6$ on DAVIS 2017 test-dev set.

Qualitative Outcomes and Comparative Analysis

The qualitative results further validate HMMN's efficacy, showcasing enhanced segmentation abilities compared to STM, CFBI, and KMN, primarily through visual improvements in test frames and video sequences. The paper illustrates these improvements with notable differences marked in dense, fast-changing scenes where temporal coherence and spatial precision are critical.

Implications and Future Directions

The findings indicate the promise of hierarchical strategies for memory enhancement in video segmentation tasks, with practical implications for automated systems requiring consistent object tracking across video data, such as surveillance or autonomous vehicles. The theoretical implications extend to the understanding of spatial-temporal networks and memory-enabled learning models. Future developments may explore more sophisticated memory hierarchies, integration with end-to-end systems, and application of HMMNs to broader contexts within artificial intelligence, potentially enhancing real-time decision-making capabilities. Additionally, employing unsupervised learning paradigms or cross-domain applications could open new avenues for exploitation of hierarchical memory structures.

Overall, this paper contributes meaningful advancements in video object segmentation, providing a synergistic approach through hierarchical memory-based network designs.