CrossKD: Cross-Head Knowledge Distillation for Object Detection (2306.11369v2)

Abstract: Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD, which delivers the intermediate features of the student's detection head to the teacher's detection head. The resulting cross-head predictions are then forced to mimic the teacher's predictions. This manner relieves the student's head from receiving contradictory supervision signals from the annotations and the teacher's predictions, greatly improving the student's detection performance. Moreover, as mimicking the teacher's predictions is the target of KD, CrossKD offers more task-oriented information in contrast with feature imitation. On MS COCO, with only prediction mimicking losses applied, our CrossKD boosts the average precision of GFL ResNet-50 with 1x training schedule from 40.2 to 43.7, outperforming all existing KD methods. In addition, our method also works well when distilling detectors with heterogeneous backbones. Code is available at https://github.com/jbwang1997/CrossKD.

An Analysis of "CrossKD: Cross-Head Knowledge Distillation for Object Detection"

The paper "CrossKD: Cross-Head Knowledge Distillation for Object Detection" introduces a novel technique aimed at improving knowledge distillation (KD) for object detectors through a framework termed Cross-Head Knowledge Distillation (CrossKD). The concept of KD, widely recognized for model compression in deep learning, translates the knowledge from a large "teacher" model to a smaller "student" model, enhancing the latter's performance while retaining efficiency in computation. This paper targets the specific challenges posed by knowledge distillation in object detection, particularly addressing the known issue of target conflict which arises during the training process between ground-truth targets and teacher predictions.

Framework and Methodology

The CrossKD framework diverges from traditional prediction mimicking paradigms, which often confront target conflict due to discrepancies between the student's annotations and the teacher’s predictions. In contrast, CrossKD alleviates these conflicts by delivering the intermediate features of the student’s detection head to the teacher’s detection head, thereby generating what they call "cross-head predictions." The distillation loss is then computed between these cross-head predictions and the original predictions of the teacher.

This cross-head approach effectively ensures that the supervision signals received by the student's head are less contradictory, leading to a more stable learning process. The paper attests to the empirical efficacy of this method by achieving superior performance on the MS COCO dataset, showcasing an increase in average precision from 40.2 to 43.7 for GFL ResNet-50 models using a 1× training schedule, outperforming existing KD techniques.

Results and Contributions

The experimentation section of the paper discusses multiple configurations where CrossKD demonstrated consistent improvements over existing KD techniques. Notably, when applied to GFL models using various backbones, CrossKD not only improved model performance significantly but also proved to be effective across heterogeneous backbone architectures. The distillation tactic is intrinsic to CrossKD's design, catering to the specific nuances of dense object detectors and highlighting its task-oriented philosophy compared to feature imitation methods.

Implications and Future Directions

The findings implicate a notable advancement in object detection efficiency through structured KD pathways like CrossKD, suggesting possible avenues for further research. The benefits of reduced target conflict suggest potential applications in scenarios where model robustness and reliability are critical, such as autonomous driving or real-time surveillance. Given the promising results, future work could explore expanding CrossKD methods to more complex architectures or integrating them with unique feature extraction techniques to further minimize discrepancies and enhance performance.

Moreover, potential adaptations of CrossKD in broader machine learning tasks beyond object detection could be a compelling direction, exploring the limits of knowledge transfer in more dynamic settings. As deep learning systems strive for computational efficiency without sacrificing accuracy, CrossKD provides a pivotal step towards reconciling these often competing objectives.

In summary, the paper provides a compelling case for re-evaluating how knowledge distillation is approached within object detection frameworks. The empirical evidence and fresh insight into mitigating target conflict present a meaningful contribution to both theoretical understanding and practical implementations in AI-driven object detection systems.