Cross-Image Relational Knowledge Distillation for Semantic Segmentation (2204.06986v2)

Abstract: Current Knowledge Distillation (KD) methods for semantic segmentation often guide the student to mimic the teacher's structured information generated from individual data samples. However, they ignore the global semantic relations among pixels across various images that are valuable for KD. This paper proposes a novel Cross-Image Relational KD (CIRKD), which focuses on transferring structured pixel-to-pixel and pixel-to-region relations among the whole images. The motivation is that a good teacher network could construct a well-structured feature space in terms of global pixel dependencies. CIRKD makes the student mimic better structured semantic relations from the teacher, thus improving the segmentation performance. Experimental results over Cityscapes, CamVid and Pascal VOC datasets demonstrate the effectiveness of our proposed approach against state-of-the-art distillation methods. The code is available at https://github.com/winycg/CIRKD.

• The paper introduces a novel cross-image relational KD approach that transfers global pixel relationships to enhance semantic segmentation.
• It leverages both pixel-to-pixel and pixel-to-region distillation with a memory bank to capture diverse inter-image semantic correlations.
• Experiments on Cityscapes, CamVid, and Pascal VOC show mIoU improvements averaging 0.78%, narrowing the performance gap between teacher and student models.

Cross-Image Relational Knowledge Distillation for Semantic Segmentation: A Summary

The paper entitled "Cross-Image Relational Knowledge Distillation for Semantic Segmentation," authored by Chuanguang Yang et al., addresses the problem of semantic segmentation with a focus on Knowledge Distillation (KD). Traditional KD approaches in this domain largely focus on guiding the student network to replicate the teacher's output on a per-image basis. However, these approaches often overlook the potential benefits of considering global semantic relations among pixels across multiple images. This paper introduces a novel approach, termed Cross-Image Relational Knowledge Distillation (CIRKD), to enhance the transfer of structured information from teacher to student networks specifically for semantic segmentation tasks.

Motivation and Methodology

The primary motivation behind CIRKD is to construct and transfer global pixel relations across a range of training images, rather than limiting the distillation process to intra-image dependencies. The premise is that a well-trained teacher network naturally organizes pixel embeddings into a structured feature space that the student network can endeavor to mimic.

CIRKD involves two key distillation strategies:

1. Pixel-to-Pixel Distillation: Both within a mini-batch and with incorporation of a memory bank, this technique captures the cross-image similarity among individual pixels. The memory bank, inspired by self-supervised learning approaches, stores abundant pixel embeddings, enabling the student to learn from a larger dataset beyond individual mini-batches.
2. Pixel-to-Region Distillation: This technique involves aggregating pixel information into region-level or class-centered embeddings, which are then used to inform the student network, thereby complementing the pixel-to-pixel relationships.

These approaches are facilitated by implementing a shared memory queue that balances the need for diverse and representative sampling across the whole dataset. Notably, segmenting global semantic relations aids in achieving improved feature representation, especially in resource-constrained environments common to lightweight models.

Experimental Evaluation

The effectiveness of CIRKD is demonstrated across multiple datasets including Cityscapes, CamVid, and Pascal VOC. The paper provides extensive evaluations comparing CIRKD to state-of-the-art methods such as SKD, IFVD, and CWD, indicating that CIRKD consistently outperforms these benchmarks across various neural network architectures, like DeepLabV3 and PSPNet, using different backbone networks (e.g., ResNet-18, MobileNetV2). Specifically, it shows substantial improvements in segmentation performance when applied to the student networks, narrowing the performance gap with the teacher networks without incurring additional computation during inference.

Numerical Results and Implications

Significantly, CIRKD achieves mIoU improvements of approximately 0.78% on average over state-of-the-art methods across tested datasets. These results support the hypothesis that structured pixel correlation knowledge, including both intra- and inter-image relations, has substantial potential for enhancing semantic segmentation tasks.

From a theoretical perspective, the approach introduces a paradigm shift in KD from focusing on local similarities to a broader, globally-informed correlation learning. Practically, the integration of CIRKD could potentially reduce the need for large, cumbersome models when deploying segmentation solutions in real-time and mobile settings, which often have constrained capabilities.

Conclusion and Future Directions

The paper asserts that cross-image relational KD provides a more exhaustive approach towards harnessing the structural knowledge of high-capacity networks. By extending the relational knowledge distillation to encapsulate a wider, cross-image context, CIRKD brings forth a novel pathway for advancing performance in semantic segmentation.

Looking forward, this work inevitably opens several avenues for further research, particularly in extending this methodology to other dense prediction tasks and exploring the integration of similar distillation processes in other domains within computer vision. There is also potential for refining the balance between computational efficiency and performance, an aspect critical to the deployment of these models in practical applications.