RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation (1611.06612v3)

Abstract: Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.

• The paper introduces a multi-path refinement network that iteratively fuses high-level semantic and fine-grained low-level features for enhanced segmentation accuracy.
• The paper employs residual connections and chained residual pooling to integrate multi-scale context and ensure effective gradient flow during training.
• The paper demonstrates state-of-the-art performance by achieving high IoU scores on benchmarks such as PASCAL VOC 2012 and Cityscapes.

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

The paper introduces RefineNet, a multi-path refinement network designed to address the challenge of high-resolution semantic segmentation. Semantic segmentation involves assigning a categorical label to each pixel in an image, which remains a demanding task due to the necessity for both high-level contextual understanding and fine-grained detail recovery. Traditional very deep convolutional neural networks (CNNs), despite their success in tasks like object recognition, often fail to maintain high image resolution due to multiple subsampling operations. RefineNet emerges as a solution to this problem by leveraging features from various depths of the network to refine high-resolution predictions iteratively.

Key Contributions

1. Multi-Path Refinement Network (RefineNet):
   • The architecture of RefineNet exploits features across multiple levels of abstraction in a cascaded manner.
   • It enhances low-resolution semantic features with fine-grained low-level features recursively, improving the resolution and accuracy of the predictions.
2. Residual Connections and Identity Mappings:
   • Inspired by residual networks, RefineNet uses residual connections for effective end-to-end training.
   • These connections facilitate gradient flow through both local short-range connections and long-range connections between layers.
3. Chained Residual Pooling:
   • This new component captures background context efficiently by pooling features from substantial image regions.
   • It employs multiple small pooling operations chained together, each refining the pooling result, and uses residual connections for effective feature fusion.

Numerical Results

RefineNet demonstrated superior performance on several established benchmarks:

• PASCAL VOC 2012: Achieved a mean Intersection-over-Union (IoU) score of 83.4, outperforming the previous best approach, DeepLab, by a significant margin.
• NYUDv2: Attained a mean IoU of 46.5, showing improvement over competing methods.
• Cityscapes: Achieved an IoU of 73.6, setting a new standard in urban scene understanding.
• Person-Part: Reached an IoU of 68.6, demonstrating effectiveness in object parsing tasks.
• PASCAL-Context: Razed a mean IoU of 47.3.
• SUN-RGBD: Noted an IoU of 45.9 without leveraging depth information.
• ADE20K: Surpassed previous methods with an IoU of 40.7.

Implications and Future Directions

Practically, RefineNet's superior performance in multiple and diverse segmentation tasks indicates its robust generalizability and applicability in real-world scenarios where high-resolution delineation is crucial, such as autonomous driving, medical imaging, and robotic vision.

Theoretically, RefineNet bridges high-level semantic features with low-level visual details, embodying the principle that multi-level feature integration fosters improved predictions. This paradigm encourages further explorations into hierarchical feature fusion and residual-based refinement strategies in neural network design.

Anticipated future directions include investigating more dynamic and adaptive refinement strategies, experimenting with novel residual architectures, and extending the approach to other dense prediction tasks like depth estimation and surface normal prediction. Given the rapid evolution of GPU capabilities, efficient memory use strategies and computational optimizations will be vital for exploring larger and more complex architectures based on RefineNet's principles.

The release of RefineNet's source code and pre-trained models will undoubtedly propel future research forward, enabling reproducibility and fostering broader adoption and adaptation of this approach in various computer vision applications.

RefineNet's contributions delineate a significant step towards resolving the trade-off between computational efficiency and high-resolution output in semantic segmentation, setting a benchmark for subsequent innovations in the field.