EffiPerception: an Efficient Framework for Various Perception Tasks

• URL: http://arxiv.org/abs/2403.12317v1
• Date: Mon, 18 Mar 2024 23:22:37 GMT
• Title: EffiPerception: an Efficient Framework for Various Perception Tasks
• Authors: Xinhao Xiang, Simon Dräger, Jiawei Zhang,
• Abstract summary: EffiPerception is a framework to explore common learning patterns and increase the module. It could achieve great accuracy robustness with relatively low memory cost under several perception tasks. EffiPerception could show great accuracy-speed-memory overall performance increase within the four detection and segmentation tasks.
• Score: 6.1522068855729755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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