EffiPerception: an Efficient Framework for Various Perception Tasks
<|reference_start|>EffiPerception: an Efficient Framework for Various Perception Tasks: 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.<|reference_end|>
@article{xiang2024effiperception:,
    title={EffiPerception: an Efficient Framework for Various Perception Tasks},
    author={Xinhao Xiang, Simon Dr"ager, Jiawei Zhang},
    journal={arXiv preprint arXiv:2403.12317},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12317},
    primaryClass={cs.CV}
}