SSAP: Single-Shot Instance Segmentation With Affinity Pyramid (1909.01616v1)

Abstract: Recently, proposal-free instance segmentation has received increasing attention due to its concise and efficient pipeline. Generally, proposal-free methods generate instance-agnostic semantic segmentation labels and instance-aware features to group pixels into different object instances. However, previous methods mostly employ separate modules for these two sub-tasks and require multiple passes for inference. We argue that treating these two sub-tasks separately is suboptimal. In fact, employing multiple separate modules significantly reduces the potential for application. The mutual benefits between the two complementary sub-tasks are also unexplored. To this end, this work proposes a single-shot proposal-free instance segmentation method that requires only one single pass for prediction. Our method is based on a pixel-pair affinity pyramid, which computes the probability that two pixels belong to the same instance in a hierarchical manner. The affinity pyramid can also be jointly learned with the semantic class labeling and achieve mutual benefits. Moreover, incorporating with the learned affinity pyramid, a novel cascaded graph partition module is presented to sequentially generate instances from coarse to fine. Unlike previous time-consuming graph partition methods, this module achieves $5\times$ speedup and 9% relative improvement on Average-Precision (AP). Our approach achieves state-of-the-art results on the challenging Cityscapes dataset.

• The paper introduces a proposal-free single-shot instance segmentation method that jointly learns semantic and instance features through an affinity pyramid.
• It employs a cascaded graph partition module to refine predictions from coarse to fine resolutions, achieving a five-fold speed improvement and a 9% AP gain on Cityscapes.
• The unified network design improves computational efficiency and scalability, delivering state-of-the-art results with 37.3% AP and 61.1% PQ on benchmark datasets.

Single-Shot Instance Segmentation with Affinity Pyramid: A Detailed Overview

The paper "SSAP: Single-Shot Instance Segmentation With Affinity Pyramid" presents a significant advancement in the domain of instance segmentation by proposing a novel methodology that circumvents the need for proposal generation, a common step in traditional approaches. This work focuses on the single-shot, proposal-free instance segmentation paradigm, highlighting its efficiency and coherent structure. The authors introduce the concept of a pixel-pair affinity pyramid and a cascaded graph partition module, effectively integrating these components into a unified network architecture.

Key Concepts and Methodology

The SSAP approach stands out by avoiding the separation of instance segmentation into distinct subtasks, such as semantic segmentation and instance feature grouping. Rather, it proposes a comprehensive methodology where these tasks are conducted in tandem within a single network pass. The cornerstone of this approach is the affinity pyramid, which evaluates the likelihood of pixel pairs belonging to the same instance through a hierarchical framework. This is a departure from previous methodologies, where separate modules were needed for each subtask, often leading to inefficiencies and increased computational overhead.

The affinity pyramid is designed to capture both short-range and long-range pixel affinities across multiple resolutions, thereby accommodating objects of varying scales and spatial configurations. The network learns affinities at multiple scales, which are crucial for dividing an image into distinct object instances. This hierarchical learning of affinities allows the network to benefit mutually from the interaction between semantic segmentation and pixel affinity learning.

A notable feature of this work is the introduction of a cascaded graph partition method, which processes the pixel affinities extracted by the pyramid. This method sequentially generates instance predictions, fine-tuning them from coarse to fine resolutions. This cascaded approach optimizes the computational process, offering significant speed improvements compared to traditional graph partitioning methods. The authors report a five-fold speed enhancement and a 9% increase in Average Precision (AP) metrics, particularly on the Cityscapes dataset, demonstrating the efficiency of this innovative approach.

Experimental Results and Comparisons

The proposed SSAP methodology yields competitive state-of-the-art results on the challenging Cityscapes dataset, attaining an AP of 37.3% on the validation set and 32.7% on the test set, alongside a Panoptic Quality (PQ) of 61.1%. These results underscore the system's capability to offer high precision and recall while maintaining computational efficiency. The inclusion of both short-range and long-range affinities in the learning process offers robust object differentiation, especially in complex urban scenes characterized by occlusions and overlapping instances.

The paper further benchmarks against various established methodologies, demonstrating significant improvements in both precision and computational efficiency. The cohesive learning of semantic labels and pixel affinities under a single integration framework not only streamlines the process but also optimizes resource utilization.

Implications and Future Directions

The introduction of a single-shot proposal-free instance segmentation framework marks a pivotal step forward in the simplification and efficiency of instance segmentation systems. By effectively leveraging the joint learning of semantic and instance-level features, the SSAP approach can potentially be expanded into more generalized computer vision applications. The hierarchy of affinities contributes to a scalable architecture capable of handling high-resolution inputs while ensuring fidelity in instance segmentation.

Future research could explore the extension of these concepts to broader domains of computer vision such as real-time processing for video data, where efficiency gains are paramount. Additionally, integrating SSAP with other advanced techniques in deep learning could further enhance its application across varied datasets and environments beyond urban scenes.

In conclusion, the SSAP framework provides compelling insights into proposal-free instance segmentation, underscoring the merits of joint task learning in achieving both high performance and computational efficiency. This work reveals new opportunities for further innovation in instance segmentation and related fields, enhancing our ability to accurately interpret complex visual data.