Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism
Continual learning is a challenging real-world problem for constructing a mature AI system when data are provided in a streaming fashion. Despite recent progress in continual classification, the researches of continual object detection are impeded by the diverse sizes and numbers of objects in each image. Different from previous works that tune the whole network for all tasks, in this work, we present a simple and flexible framework for continual object detection via pRotOtypical taSk corrElaTion guided gaTing mechAnism (ROSETTA). Concretely, a unified framework is shared by all tasks while task-aware gates are introduced to automatically select sub-models for specific tasks. In this way, various knowledge can be successively memorized by storing their corresponding sub-model weights in this system. To make ROSETTA automatically determine which experience is available and useful, a prototypical task correlation guided Gating Diversity Controller(GDC) is introduced to adaptively adjust the diversity of gates for the new task based on class-specific prototypes. GDC module computes class-to-class correlation matrix to depict the cross-task correlation, and hereby activates more exclusive gates for the new task if a significant domain gap is observed. Comprehensive experiments on COCO-VOC, KITTI-Kitchen, class-incremental detection on VOC and sequential learning of four tasks show that ROSETTA yields state-of-the-art performance on both task-based and class-based continual object detection.
Code (1)
Tasks
Continual LearningDiversityObjectobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
Continual Few-shot Event Detection via Hierarchical Augmentation Networks
Traditional continual event detection relies on abundant labeled data for training, which is often impractical to obtain in real-world applications. In this paper, we introduce continual few-shot event detection (CFED), …
Event DetectionSteering Prototypes with Prompt-tuning for Rehearsal-free Continual Learning
In the context of continual learning, prototypes-as representative class embeddings-offer advantages in memory conservation and the mitigation of catastrophic forgetting. However, challenges related to semantic drift and…
class-incremental learningClass Incremental LearningContinual LearningContrastive Learning+1CP-VoteNet: Contrastive Prototypical VoteNet for Few-Shot Point Cloud Object Detection
Few-shot point cloud 3D object detection (FS3D) aims to identify and localise objects of novel classes from point clouds, using knowledge learnt from annotated base classes and novel classes with very few annotations. Th…
3D Object Detectionobject-detectionObject DetectionPrototypical Variational Autoencoder for 3D Few-shot Object Detection
Few-Shot 3D Point Cloud Object Detection (FS3D) is a challenging task, aiming to detect 3D objects of novel classes using only limited annotated samples for training. Considering that the detection performance highly rel…
ICICLE: Interpretable Class Incremental Continual Learning
Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks. However, continua…
class-incremental learningClass Incremental LearningContinual LearningExemplar-Free+2