Xin Lv

Continual learning for open-vocabulary object detection aims to enable pretrained vision–language detectors to adapt to diverse specialized domains while preserving their zero-shot generalization capabilities. However, existing methods primarily focus on mitigating catastrophic forgetting, often neglecting the substantial domain shifts commonly encountered in real-world applications. To address this critical oversight, we pioneer Open-Domain Continual Object Detection (OD-COD), a new paradigm that requires detectors to continually adapt across domains with significant stylistic gaps. We propose Disentangled Domain Knowledge-Aided Learning (D-Know) to tackle this challenge. This framework explicitly disentangles domain-general priors from category-specific adaptation, managing them dynamically in a scalable domain knowledge base. Specifically, D-Know first learns domain priors in a self-supervised manner and then leverages these priors to facilitate category-specific adaptation within each domain. To rigorously evaluate this task, we construct OD-CODB, the first dedicated benchmark spanning six domains with substantial visual variations. Extensive experiments demonstrate that D-Know achieves superior performance, surpassing current state-of-the-art methods by an average of 4.2% mAP under open-domain continual settings while maintaining strong zero-shot generalization. Furthermore, experiments under the few-shot setting confirm D-Know’s superior data efficiency.

Convolutional Neural Networks (CNNs) have achieved remarkable performance in computer vision tasks, but their deployment on resource-constrained devices remains challenging. While existing lightweight CNNs reduce FLOPs significantly, their practical inference speed is limited by memory access bottlenecks. We hence propose CFNet, an efficient architecture that bridges the gap between theoretical efficiency and practical speed through synergistic design of channel-focused convolution (CFConv) and channel mixed unit (CMU). CFConv dynamically selects informative channels via learnable GroupNorm scaling factors and reparameterization, reducing both FLOPs and memory access, while CMU enables cross-channel communication through a split-transform-and-mix strategy to mitigate information loss. Experiments on CIFAR/ImageNet classification and MS COCO object detection demonstrate CFNet’s superior performance. On ImageNet-1K, CFNet-A achieves 35.5% and 189.4% GPU throughput improvements over MobileNetV2 and MobileViTv1-XXS respectively, while delivering 1.76% and 4.09% accuracy gains. CFNet-E attains 83.5% top-1 accuracy, outperforming Swin-S by 0.47% with 44.6% higher GPU throughput and 43.6% lower CPU inference latency.