HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images (2112.09131v2)

Abstract: Existing state-of-the-art methods for Video Object Segmentation (VOS) learn low-level pixel-to-pixel correspondences between frames to propagate object masks across video. This requires a large amount of densely annotated video data, which is costly to annotate, and largely redundant since frames within a video are highly correlated. In light of this, we propose HODOR: a novel method that tackles VOS by effectively leveraging annotated static images for understanding object appearance and scene context. We encode object instances and scene information from an image frame into robust high-level descriptors which can then be used to re-segment those objects in different frames. As a result, HODOR achieves state-of-the-art performance on the DAVIS and YouTube-VOS benchmarks compared to existing methods trained without video annotations. Without any architectural modification, HODOR can also learn from video context around single annotated video frames by utilizing cyclic consistency, whereas other methods rely on dense, temporally consistent annotations. Source code is available at: https://github.com/Ali2500/HODOR

• The paper demonstrates that high-level object descriptors learned from static images can robustly re-segment objects in video, matching state-of-the-art performance.
• The method eliminates the need for labor-intensive dense video annotations by leveraging abundant annotated static image datasets.
• Empirical results on DAVIS and YouTube-VOS benchmarks show that HODOR achieves over 81% J&F score, highlighting its efficiency in video object segmentation.

Overview of "HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images"

The paper introduces HODOR, a novel method for video object segmentation (VOS) that leverages annotated static images to overcome limitations associated with data-intensive video annotation processes traditionally required for state-of-the-art VOS methods. The researchers propose using high-level object descriptors to facilitate object segmentation across different video frames, contrasting with conventional methods that rely on low-level pixel-to-pixel correspondences.

Key Contributions and Methodology

1. High-level Object Descriptors:
   • HODOR focuses on encoding object instances and scene context from static images into robust descriptors. These descriptors serve as high-level summaries of object appearances and allow for re-segmentation across video frames without the need for densely annotated video data.
2. Eliminating Dense Video Annotations:
   • Traditional VOS methods depend heavily on dense video annotations which are labor-intensive and often redundant due to frame similarity. HODOR bypasses this by using static image datasets, opening up access to hundreds of thousands of labeled images compared to the few thousand videos available in existing datasets.
3. Architecture:
   • The HODOR framework encompasses a backbone that learns the image features, a high-level object descriptor (HOD) encoder, and an object re-segmentation (OR) decoder.
   • The encoder produces descriptors by processing input masks and image features, whereas the decoder uses these descriptors to segment objects in different frames by conditioning them on new image features.
4. Training from Static Images and Unlabeled Frames:
   • HODOR can be trained using static image annotations without additional synthetic augmentations. Moreover, the method supports learning from video contexts around single annotated frames through cyclic consistency. This adaptability allows the network to learn robust descriptors effectively, even from sparsely annotated video data.
5. Simultaneous Multi-object Processing:
   • The encoder can model interactions between an arbitrary number of objects, optimizing the inference speed and performance compared to methodologies requiring separate processing for each object.
6. State-of-the-art Performance:
   • Empirically, HODOR achieves top performance on DAVIS and YouTube-VOS benchmarks among methods trained without video annotations, demonstrating the efficacy of learning from high-level descriptors.

Results and Implications

HODOR reaches a notable performance level of over 81% J&F on the DAVIS benchmark, indicating comparable effectiveness to video-trained methods without relying on dense video annotations. This marks a significant advancement in VOS, providing a viable alternative to resource-heavy training processes and proving the utility of static images in understanding object features for dynamic segmentation tasks.

Future Prospects

The high-level descriptor approach proposed by HODOR suggests potential for future developments in AI and computer vision, particularly in contexts where video data is sparse or expensive to acquire. The implications extend to broader applications in visual understanding tasks across uncharted object categories and complex scenes, potentially improving models' generalization and robustness.

Conclusion

The research in this paper charts a promising direction in VOS by fundamentally shifting from traditional dense annotation reliance to leveraging the abundant available annotated static imagery. By refining how object appearance and contextual data are encoded, HODOR sets the stage for more accessible, scalable, and efficient AI-driven video analysis.