Egocentric segmentation has attracted recent interest in the computer vision
community due to their potential in Mixed Reality (MR) applications. While most
previous works have been focused on segmenting egocentric human body parts
(mainly hands), little attention has been given to egocentric objects. Due to
the lack of datasets of pixel-wise annotations of egocentric objects, in this
paper we contribute with a semantic-wise labeling of a subset of 2124 images
from the RGB-D THU-READ Dataset. We also report benchmarking results using
Thundernet, a real-time semantic segmentation network, that could allow future
integration with end-to-end MR applications.

本文针对近年来计算机视觉领域中关于自我中心图像物体分割技术的研究，受其在混合现实应用中的重要性所驱动，提供了一个基于 RGB-D THU-READ 数据集的 2124 个图像的语义级别标注，并使用 Thundernet 进行了基准测试，为未来相应的端到端混合现实应用提供了一定的参考价值。

实时自我中心物体分割：清华大学读取标签和基准测试结果

Real Time Egocentric Object Segmentation: THU-READ Labeling and  Benchmarking Results

Real-time generic object detection on mobile platforms is a crucial but
challenging computer vision task. However, previous CNN-based detectors suffer
from enormous computational cost, which hinders them from real-time inference
in computation-constrained scenarios. In this paper, we investigate the
effectiveness of two-stage detectors in real-time generic detection and propose
a lightweight two-stage detector named ThunderNet. In the backbone part, we
analyze the drawbacks in previous lightweight backbones and present a
lightweight backbone designed for object detection. In the detection part, we
exploit an extremely efficient RPN and detection head design. To generate more
discriminative feature representation, we design two efficient architecture
blocks, Context Enhancement Module and Spatial Attention Module. At last, we
investigate the balance between the input resolution, the backbone, and the
detection head. Compared with lightweight one-stage detectors, ThunderNet
achieves superior performance with only 40% of the computational cost on PASCAL
VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps
on an ARM-based device. To the best of our knowledge, this is the first
real-time detector reported on ARM platforms. Our code and models are available
at https://github.com/qinzheng93/ThunderNet.

本研究针对移动平台上实时通用物体检测这一计算机视觉任务存在的计算成本巨大的问题，调查了两阶段检测器在实时检测方面的有效性，并提出了一个名为 ThunderNet 的轻量级两阶段检测器，相对于单阶段检测器，仅使用了 40％的计算且取得了优越的性能，在 ARM 平台上实现了 24.1fps 的实时检测。