YOLOP: You Only Look Once for Panoptic Driving Perception

YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception

Over the last decade, multi-tasking learning approaches have achieved promising results in solving panoptic driving perception problems, providing both high-precision and high-efficiency performance. It has become a popular paradigm when designing networks for real-time practical autonomous driving system, where computation resources are limited. This paper proposed an effective and efficient multi-task learning network to simultaneously perform the task of traffic object detection, drivable road area segmentation and lane detection. Our model achieved the new state-of-the-art (SOTA) performan…

Q-YOLOP：面向全景驾驶感知的量化感知 You Only Look Once 模型

Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception

本文提出量化感知的全景驾驶感知模型 Q-YOLOP，采用 ELAN 骨干网络和四阶段训练策略，在 BDD100K 与 iVS 数据集上实现目标检测与分割的领先性能。

You Only Segment Once: Towards Real-Time Panoptic Segmentation

In this paper, we propose YOSO, a real-time panoptic segmentation framework. YOSO predicts masks via dynamic convolutions between panoptic kernels and image feature maps, in which you only need to segment once for both instance and semantic segmentation tasks. To reduce the computational overhead, we design a feature pyramid aggregator for the feature map extraction, and a separable dynamic decoder for the panoptic kernel generation. The aggregator re-parameterizes interpolation-first modules in a convolution-first way, which significantly speeds up the pipeline without any additional costs. T…

OneFormer: One Transformer to Rule Universal Image Segmentation

Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation in the last decades include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures. However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance. Ideally, a truly universal framework should be trained only once and achieve SOTA performance across all three image segmentation tasks. To that end, we propose OneFormer, a universal imag…

一次性对齐与提示一切以实现通用视觉感知

Aligning and Prompting Everything All at Once for Universal Visual Perception

本文提出通用视觉感知模型APE，通过将定位重构为开放词汇检测、将语义与全景分割统一为代理实例学习，一次性对齐和提示图像中的一切，在160多个数据集上以单一权重达到领先性能。