This paper introduces a Boundary and Context Distillation (BCD) method for efficient dense image prediction, enhancing compact models’ boundary completeness and region connectivity through targeted knowledge transfer, achieving superior accuracy across multiple tasks and datasets without inference cost increase.
Knowledge Distillation, Semantic Segmentation, Object Detection, Instance Segmentation, Efficiency, Representation Learning
Dong Zhang, Pingcheng Dong, Long Chen, Kwang-Ting Cheng
Hong Kong University of Science and Technology (HKUST)
Generated by grok-3
Background Problem
Dense image prediction (DIP) tasks, such as semantic segmentation, object detection, and instance segmentation, are critical in computer vision but challenging due to the high computational and memory demands of large, accurate models when deployed on resource-constrained edge devices. Efficient DIP (EDIP) models, created via knowledge distillation (KD), often struggle with maintaining boundary region completeness and preserving target region connectivity, despite being adept at recognizing main object regions. This work aims to address these specific shortcomings by proposing a targeted KD strategy to enhance the performance of compact student models without increasing inference costs.
Method
The proposed method, Boundary and Context Distillation (BCD), is a complementary KD strategy tailored for EDIP tasks. It consists of two main components:
- Boundary Distillation: This extracts explicit object-level semantic boundaries from hierarchical feature maps of the backbone network using semantic affinity similarity between pixel pairs, formulated as a loss function to enhance the student model’s mask quality in boundary regions (Eq. 3 in the paper). Unlike prior methods, it does not require pre-extracted ground-truth boundaries, reducing noise and labor.
- Context Distillation: This transfers implicit pixel-level contextual information from the teacher to the student model via self-relations computed over concatenated features (Eq. 4 and 5), ensuring robust connectivity in target regions. It operates in a whole-to-whole manner, avoiding layer-to-layer noise. The overall loss combines standard supervision loss with weighted BCD losses, using a weight-decay strategy to balance reliance on ground truth over epochs (Eq. 8). This method is designed for simplicity and efficiency, focusing on task-specific knowledge transfer without inference overhead.
Experiment
The experiments were conducted on five challenging datasets (Pascal VOC 2012, Cityscapes, ADE20K, COCO-Stuff 10K for semantic segmentation, and MS-COCO 2017 for instance segmentation and object detection) using various teacher-student model pairs (e.g., PSPNet-101 to PSPNet-18, Mask2Former to SegFormer-B0). The setup included standard data augmentation and evaluation metrics like mIoU for segmentation and AP for detection/segmentation tasks. Results showed consistent accuracy improvements across tasks and architectures, with mIoU gains up to 4.53% (EfficientNet-B1 on Pascal VOC) and AP improvements averaging 0.5% over state-of-the-art (SOTA) methods on MS-COCO. The design was comprehensive, testing heterogeneous architectures and joint implementations with other KD methods, demonstrating generalizability. However, some gains were marginal (e.g., 0.32% mIoU on COCO-Stuff 10K), and the paper lacks detailed analysis of training overhead for BCD computations. Visualizations confirmed better boundary and connectivity predictions, though some failure cases persisted due to dataset-specific correlations. Overall, results matched the expectation of targeted improvement but with room for deeper critical evaluation of practical impact versus cost.
Further Thoughts
The BCD approach opens intriguing avenues for task-specific knowledge distillation, particularly in addressing nuanced failure modes in EDIP tasks. However, its reliance on feature-derived boundaries without ground truth might limit robustness in scenarios with high semantic ambiguity or diverse datasets—could integrating minimal ground-truth supervision during training enhance reliability without sacrificing practicality? Additionally, the pixel-level context distillation, while innovative, might benefit from adaptive mechanisms to filter noise in self-relations, perhaps by incorporating attention mechanisms from transformer architectures to prioritize relevant contextual cues. I also see potential in exploring BCD’s applicability to emerging vision foundation models like SAM (Segment Anything Model), as hinted in the conclusion, especially for zero-shot or few-shot dense prediction tasks on edge devices. This could bridge the gap between large pre-trained models and resource-constrained environments, a critical area given the growing deployment of AI in IoT. Lastly, comparing BCD’s training overhead with lightweight alternatives like parameter-efficient fine-tuning methods (e.g., LoRA) could provide insights into its scalability for real-world applications.