Motion-Attentive Transition for Zero-Shot Video Object Segmentation (2003.04253v3)

Abstract: In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation. An asymmetric attention block, called Motion-Attentive Transition (MAT), is designed within a two-stream encoder, which transforms appearance features into motion-attentive representations at each convolutional stage. In this way, the encoder becomes deeply interleaved, allowing for closely hierarchical interactions between object motion and appearance. This is superior to the typical two-stream architecture, which treats motion and appearance separately in each stream and often suffers from overfitting to appearance information. Additionally, a bridge network is proposed to obtain a compact, discriminative and scale-sensitive representation for multi-level encoder features, which is further fed into a decoder to achieve segmentation results. Extensive experiments on three challenging public benchmarks (i.e. DAVIS-16, FBMS and Youtube-Objects) show that our model achieves compelling performance against the state-of-the-arts.

Overview of Motion-Attentive Transition for Zero-Shot Video Object Segmentation

The paper entitled "Motion-Attentive Transition for Zero-Shot Video Object Segmentation" introduces Motion-Attentive Transition Network (MATNet), a novel approach for automatic video object segmentation that expands the capabilities of deep learning models within the zero-shot framework. This research addresses the challenges intrinsic to video data, such as appearance variations and background clutter, by pioneering a method to reinforce spatio-temporal representation through hierarchical integration of motion and appearance cues.

Key Contributions

MATNet introduces several innovative components across its architecture to improve video object segmentation:

1. Motion-Attentive Transition (MAT) Block: The MAT block plays a central role in enhancing object representation by interleaving motion attention within appearance features. By using an asymmetric attention mechanism, MAT blocks segregate the significance of motion cues, which are then transferred to appearance features, enabling a unified spatio-temporal representation that mitigates the risks of overfitting to appearance alone.
2. Scale-Sensitive Attention (SSA): SSA bridges the encoder and decoder networks, transforming multi-level encoder features into a compact, discriminative representation. The SSA utilizes a two-level attention scheme—local for preserving object regions and global for recalibrating scale-sensitive features—further fed into the decoder to refine segmentation details.
3. Boundary-Aware Refinement (BAR) Module: The BAR modules enhance segmentation accuracy by integrating multi-scale features, employing object boundary prediction through hard example mining (HEM). This systematic attention to boundary details allows the decoder to achieve finer structural outputs.

Experimental Validation and Implications

The proposed MATNet shows considerable improvement over existing methods across several benchmarks, including DAVIS-16, FBMS, and Youtube-Objects, as detailed through extensive experimentation. Notably, MATNet achieves an 82.4% region similarity (Mean $\mathcal{J}$) on DAVIS-16, marking a significant enhancement in segmentation performance without requiring human-annotated training data. Such advancements underline its practical applications in areas such as autonomous driving, surveillance, and video analytics.

Future Directions

While MATNet provides a robust framework by leveraging motion cues integratively with appearance features, future work could explore its adoption in broader video analysis tasks, such as action recognition and real-time annotation. Additionally, adapting MATNet for environments with occluded or overlapping objects may enhance its applicability in challenging real-world scenarios.

In summary, the sophistication of MATNet with its interleaved encoding and attentive transitions forms a basis for advanced video comprehension, reinforcing the trajectory toward fully automated video object segmentation without prerequisite annotations. This work not only augments the existing frameworks but also sets a precedent for subsequent explorations in zero-shot learning paradigms.