FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation (1902.09513v2)

Abstract: Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use. In this work, we propose FEELVOS as a simple and fast method which does not rely on fine-tuning. In order to segment a video, for each frame FEELVOS uses a semantic pixel-wise embedding together with a global and a local matching mechanism to transfer information from the first frame and from the previous frame of the video to the current frame. In contrast to previous work, our embedding is only used as an internal guidance of a convolutional network. Our novel dynamic segmentation head allows us to train the network, including the embedding, end-to-end for the multiple object segmentation task with a cross entropy loss. We achieve a new state of the art in video object segmentation without fine-tuning with a J&F measure of 71.5% on the DAVIS 2017 validation set. We make our code and models available at https://github.com/tensorflow/models/tree/master/research/feelvos.

• The paper achieves state-of-the-art video object segmentation by eliminating the need for first-frame fine-tuning.
• It employs global and local pixel-wise embedding matching to effectively propagate segmentation information across frames.
• The approach processes frames in 0.51 seconds and attains a 71.5% 𝒥𝒻 score on DAVIS 2017, demonstrating real-time applicability.

An Expert Overview of "FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation"

The paper "FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation" addresses a critical need within the video object segmentation (VOS) domain: developing a method that balances simplicity, speed, and accuracy without requiring fine-tuning. The authors introduce FEELVOS, a novel approach that leverages pixel-wise embedding and matching mechanisms to transfer segmentation information effectively throughout a video sequence.

Contribution and Methodology

FEELVOS stands out by eschewing the reliance on first-frame fine-tuning, a common practice in many VOS methods that significantly increases computational overhead. Instead, the proposed method uses a semantic pixel-wise embedding combined with both global and local matching mechanisms. These mechanisms transfer information from the annotated first frame and the preceding frame to guide the segmentation of subsequent frames.

Key components include:

1. Global Matching: By learning a pixel-wise embedding, FEELVOS performs global nearest neighbor matching between the current frame and the first frame's embeddings. This provides a baseline for segmentation without entailing the computational cost of fine-tuning.
2. Local Matching: To account for temporal dynamics, FEELVOS employs local matching with the previous frame’s embeddings, exploiting the typically minimal frame-to-frame object displacement, which helps mitigate errors from global matching alone.
3. Dynamic Segmentation Head: The network is trained with a novel dynamic segmentation head that systematically processes multiple objects and produces segmentation results in a single forward pass, maintaining efficiency even with varying object counts.

The paper claims a new state-of-the-art performance in VOS without fine-tuning, reporting a $\mathcal{J}${content}$\mathcal{F}$ measure of 71.5% on the DAVIS 2017 validation set—a significant improvement over previous non-fine-tuning methods.

Results and Implications

The measurable performance gains of FEELVOS indicate that embedding-based matching can offer robust guidance to convolutional networks in the absence of resource-intensive fine-tuning. The method’s ability to maintain competitive segmentation accuracy while processing frames in 0.51 seconds per frame suggests practical applicability in real-time VOS scenarios such as robotics and autonomous vehicles.

FEELVOS emphasizes embedding learning and end-to-end training in modern computer vision—providing a template for future works looking to optimize VOS architectures while maintaining simplicity and performance. The methods employed here may inspire developments in related domains like video tracking and multi-object segmentation, potentially reducing the reliance on complex pipelines and lengthy inference times.

The paper also opens avenues for future research into enhancing embedding quality and refinement processes in segmentation heads, especially in handling complex scenes with occlusions or similar-looking objects.

Conclusion

In conclusion, FEELVOS represents a significant step toward practical VOS solutions, marrying the desiderata of speed, simplicity, and efficacy without the trade-offs traditionally necessitated by fine-tuning processes. Its architectural insights propel both theoretical and practical dialogues on advancing VOS techniques, advocating for efficiency-driven solutions in broader AI applications.