Gradient Decomposition and Alignment for Incremental Object Detection

Incremental object detection (IOD) is crucial for enabling AI systems to continuously learn new object classes over time while retaining knowledge of previously learned categories, allowing model to adapt to dynamic environments without forgetting prior information. Existing IOD methods primarily employ knowledge distillation to mitigate catastrophic forgetting, yet these approaches overlook class overlap issues, often resulting in suboptimal performance. In this paper, we propose a novel framework for IOD that leverages a decoupled gradient alignment technique on top of the specially proposed pseudo-labeling strategy.

Our method employs a Gaussian Mixture Model to accurately estimate pseudo-labels of previously learned objects in current training images, effectively functioning as a knowledge-replay mechanism. This strategy reinforces prior knowledge retention and prevents the misclassification of unannotated foreground objects from earlier classes as background. Furthermore, we introduce an adaptive gradient decomposition and alignment method to maintain model stability while facilitating positive knowledge transfer. By aligning gradients from both old and new classes, our approach preserves previously learned knowledge while enhancing plasticity for new tasks. Extensive experiments on two IOD benchmarks demonstrate the effectiveness of the proposed method, achieving superior performances to state-of-the-art methods. The code and datasets are available at https://github.com/FHR-L/GDA-IOD.