Recurrent Instance Segmentation (1511.08250v3)

Abstract: Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image. Current instance segmentation approaches consist of ensembles of modules that are trained independently of each other, thus missing opportunities for joint learning. Here we propose a new instance segmentation paradigm consisting in an end-to-end method that learns how to segment instances sequentially. The model is based on a recurrent neural network that sequentially finds objects and their segmentations one at a time. This net is provided with a spatial memory that keeps track of what pixels have been explained and allows occlusion handling. In order to train the model we designed a principled loss function that accurately represents the properties of the instance segmentation problem. In the experiments carried out, we found that our method outperforms recent approaches on multiple person segmentation, and all state of the art approaches on the Plant Phenotyping dataset for leaf counting.

• The paper presents an innovative end-to-end recurrent neural network model that sequentially segments individual image instances while effectively handling occlusion.
• The approach integrates a fully convolutional network with ConvLSTM units to iteratively generate segmentation masks that leverage spatial memory for precise delineation.
• Experimental evaluations on datasets like Pascal VOC 2012 and CVPPP demonstrate that the model achieves state-of-the-art performance without relying on separate, heuristic modules.

Recurrent Instance Segmentation

The paper "Recurrent Instance Segmentation" by Bernardino Romera-Paredes and Philip Hilaire Sean Torr introduces an innovative approach to instance segmentation through a novel end-to-end method that leverages recurrent neural networks (RNNs). This technique is designed to address the shortcomings of conventional instance segmentation models, which rely on independent modules that do not fully exploit joint learning opportunities.

Summary of the Approach

Instance segmentation necessitates distinguishing and delineating individual objects within an image, a task complicated by the lack of a priori knowledge about the number of instances. Traditional methods involve multipart systems where object proposal, recognition, and segmentation are separate modules. The paper highlights that these modular approaches miss out on potential performance improvements due to the lack of integrated learning.

The authors propose an end-to-end model using a recurrent neural network that segments each instance one by one, maintaining a spatial memory of processed pixels to manage occlusion. This sequential process mimics human counting, which is conducted iteratively with an accurate spatial memory reference, as demonstrated in cognitive studies.

Methodology

The model utilizes an RNN structured around ConvLSTM units, adapted for spatial tasks by replacing fully connected layers with convolutional layers, which suit image data better. The sequential segmentation follows these steps:

1. An image is inputted to a fully convolutional network (FCN) that produces feature maps.
2. The ConvLSTM processes these feature maps iteratively, generating a segmentation mask for one instance at each step.
3. Each iteration considers the hidden state that evolves, allowing the network to implicitly handle occlusion and previously segmented areas.

Training utilizes a loss function sensitive to instance segmentation's intricacies, emphasizing the permutation-invariance characteristic of the problem. The derived loss integrates an intersection-over-union metric with a mechanism for managing predictions' confidence to ensure efficient training and stopping conditions in inference.

Experimental Outcomes and Evaluation

The proposed model's competence is demonstrated in two experimental setups:

• Multiple Person Segmentation: Integrated with the existing FCN-8s network, the model is benchmarked against state-of-the-art methods on the Pascal VOC 2012 dataset, showing comparable or superior performance, especially when post-processed with CRFs.
• Plant Leaf Segmentation and Counting: Using the CVPPP dataset, the model showcases exceptional counting accuracy, outperforming task-specific methods despite being trained from scratch, illustrating its adaptation to the domain without heuristic constraints.

Discussion and Implications

The paper posits that the end-to-end nature of the proposed recurrent model significantly contributes to instance segmentation, facilitating solution simplicity and efficiency by eliminating dependence on independent module coordination and heuristic processes. The model's architecture and loss function play pivotal roles in accommodating the complex spatial dependencies and permutation challenges inherent in instance segmentation.

The research opens new horizons for further exploration in AI, such as the inclusion of multi-class segmentation/classification within the recurrent model and advancements in recurrent structure optics for enhanced sequential image analysis. Integrating CRF-based refinement directly into the learning pipeline could enhance segmentation boundary accuracy, and the exploration of new architectures may yield even more robust systems.

Conclusion

Through this paper, a meaningful stride has been made in moving towards fully integrated, sequential instance segmentation systems, illuminating paths to future research that could explore classification extensions and leverage recurrent layers to integrate object semantics more cohesively with spatial delineation tasks.