Self-Reflection Neural Network for Class-Incremental Object Counting

In crowded scenarios, achieving the counting task of dynamically evolving categories is extremely challenging. In addition to grappling with challenges such as scale variations, severe occlusion and complex backgrounds, it is imperative to mitigate the issue of catastrophic forgetting. Previous approaches have heavily relied on leveraging historical data for knowledge distillation to tackle these difficulties. However, this strategy encounters two prominent obstacles: 1) Employing the teacher network from the previous stage for distillation incurs additional computational overhead during the training stage. 2) Although knowledge distillation can facilitate effective knowledge transfer, some inaccurate predictions from the teacher network may affect the knowledge acquisition in the current stage. To overcome these issues, we introduce a novel solution: a self-reflection neural network for class-incremental object counting. First, we construct a global-aware incremental regression branch that uses stacked transformer layers as backends to capture global information, while the final regression layers dynamically expand as categories increase. Furthermore, we introduce an uncertain estimation branch that selectively isolates certain feature maps to avoid some neurons updated with excessive gradient information, thereby enhancing the network plasticity while preserving stability. The output of this branch functions as a regularization signal, steering the learning process of the incremental regression branch. To foster a more robust retention of past knowledge, we propose a self-reflection loss. It employs the rectified outputs of global-aware incremental regression branch to encourage the network to reflect upon and refine its grasp of historical knowledge, effectively averting the pitfalls of inaccurate information. Our extensive experiments validate the effectiveness of our proposed method, achieving state-of-the-art results.