Making a Case for 3D Convolutions for Object Segmentation in Videos (2008.11516v2)

Abstract: The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of information. On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance. In this work, we show that 3D CNNs can be effectively applied to dense video prediction tasks such as salient object segmentation. We propose a simple yet effective encoder-decoder network architecture consisting entirely of 3D convolutions that can be trained end-to-end using a standard cross-entropy loss. To this end, we leverage an efficient 3D encoder, and propose a 3D decoder architecture, that comprises novel 3D Global Convolution layers and 3D Refinement modules. Our approach outperforms existing state-of-the-arts by a large margin on the DAVIS'16 Unsupervised, FBMS and ViSal dataset benchmarks in addition to being faster, thus showing that our architecture can efficiently learn expressive spatio-temporal features and produce high quality video segmentation masks. We have made our code and trained models publicly available at https://github.com/sabarim/3DC-Seg.

• The paper introduces an end-to-end 3D CNN architecture with GC3D and 3D Refinement layers to capture detailed spatio-temporal features.
• It demonstrates superior segmentation performance, achieving an 84.1% J-mean and F-mean on the DAVIS'16 dataset over conventional 2D methods.
• The model processes frames at 0.22 seconds each, proving that efficient design can deliver high accuracy without compromising speed.

A Case for 3D Convolutions in Video Object Segmentation

Recent advancements in computer vision have leveraged the power of deep learning to tackle various complex tasks, including object segmentation in videos. This paper focuses on the potential of 3D Convolutional Neural Networks (3D CNNs) for dense per-pixel video prediction tasks such as salient object segmentation, typically approached using 2D networks handling appearance and motion separately. While 3D CNNs have been successful in video classification, their applicability to video segmentation remained underexplored due to computational challenges and difficulty in achieving fine-grained, pixel-level accuracy.

The paper proposes an end-to-end trainable 3D CNN-based encoder-decoder architecture designed to overcome these barriers. The architecture employs 3D convolutions throughout, leveraging novel 3D Global Convolutional (GC3D) layers and 3D Refinement modules to capture and refine spatio-temporal features effectively. The model's encoder utilizes a lightweight yet expressive architecture, pre-trained on large-scale action classification datasets to embody a broad receptive field, essential for accurate segmentation.

Key Findings

1. Performance Gains: The proposed model significantly surpasses traditional 2D CNN frameworks. On established benchmarks such as DAVIS'16 Unsupervised, FBMS, and ViSal datasets, it achieves superior segmentation accuracy. Notably, the model achieves a 84.1% $\mathcal{J}$-mean and $\mathcal{F}$-mean on the DAVIS'16 dataset, marking a distinctive improvement over previous state-of-the-art methods.
2. Efficiency: Despite its depth, the network maintains efficiency by significantly reducing the number of parameters and still achieves fast inference times. Approximately 0.22 seconds per frame processing speed was recorded on standard hardware, demonstrating that 3D convolutions, when implemented efficiently, do not necessarily trade off computational speed for performance.
3. Module Effectiveness: Incorporating the GC3D and 3D Refinement layers resulted in considerable performance benefits, enhancing the model's capability to learn and refine complex hierarchical features necessary for dynamic and varied video content.

Implications and Future Directions

This research offers compelling evidence for reconsidering 3D CNNs in video segmentation, paving the way for further developments that leverage both temporal and spatial data more integrally. The proposed encoder-decoder setup serves as a baseline for future research on related tasks, such as video-based object discovery, multi-object tracking, and video instance segmentation.

Further investigation could enhance the architecture to handle higher-dimensional datasets, potentially employing optimization techniques to further refine computational efficiency. Exploring the model’s adaptability to real-time tasks or integration with additional sensory modalities might also prove beneficial for autonomous systems and robotics.

The paper's contribution lies in driving a shift towards integrated networks that capitalize on the holistic modeling of motion and appearance - a likely trajectory in the evolving landscape of video understanding tasks in computer vision.