EffiPerception: an Efficient Framework for Various Perception Tasks
This work addresses efficiency challenges for computer vision practitioners, but it appears incremental as it builds on existing methods with optimizations.
The authors tackled the accuracy-speed-memory trade-off in computer vision perception tasks by proposing the EffiPerception framework, which achieved improved overall performance across 2D/3D object detection and segmentation tasks on datasets like KITTI and COCO compared to earlier methods.
The accuracy-speed-memory trade-off is always the priority to consider for several computer vision perception tasks. Previous methods mainly focus on a single or small couple of these tasks, such as creating effective data augmentation, feature extractor, learning strategies, etc. These approaches, however, could be inherently task-specific: their proposed model's performance may depend on a specific perception task or a dataset. Targeting to explore common learning patterns and increasing the module robustness, we propose the EffiPerception framework. It could achieve great accuracy-speed performance with relatively low memory cost under several perception tasks: 2D Object Detection, 3D Object Detection, 2D Instance Segmentation, and 3D Point Cloud Segmentation. Overall, the framework consists of three parts: (1) Efficient Feature Extractors, which extract the input features for each modality. (2) Efficient Layers, plug-in plug-out layers that further process the feature representation, aggregating core learned information while pruning noisy proposals. (3) The EffiOptim, an 8-bit optimizer to further cut down the computational cost and facilitate performance stability. Extensive experiments on the KITTI, semantic-KITTI, and COCO datasets revealed that EffiPerception could show great accuracy-speed-memory overall performance increase within the four detection and segmentation tasks, in comparison to earlier, well-respected methods.