The recently proposed segment anything model (SAM) has made a significant
influence in many computer vision tasks. It is becoming a foundation step for
many high-level tasks, like image segmentation, image caption, and image
editing. However, its huge computation costs prevent it from wider applications
in industry scenarios. The computation mainly comes from the Transformer
architecture at high-resolution inputs. In this paper, we propose a speed-up
alternative method for this fundamental task with comparable performance. By
reformulating the task as segments-generation and prompting, we find that a
regular CNN detector with an instance segmentation branch can also accomplish
this task well. Specifically, we convert this task to the well-studied instance
segmentation task and directly train the existing instance segmentation method
using only 1/50 of the SA-1B dataset published by SAM authors. With our method,
we achieve a comparable performance with the SAM method at 50 times higher
run-time speed. We give sufficient experimental results to demonstrate its
effectiveness. The codes and demos will be released at
this https URL

本论文针对计算机视觉中处理高分辨率输入带来的高昂计算代价提出了使用基于 CNN 检测器结合实例分割进行图像分割的替代方案，实验结果表明该方法仅使用 SAM 作者发布的 1/50 数据集，便可在 50 倍的运行时速下实现与 SAM 方法基本相当的性能。

快速分割任何物体

Fast Segment Anything

The recently proposed pseudo-LiDAR based 3D detectors greatly improve the
benchmark of monocular/stereo 3D detection task. However, the underlying
mechanism remains obscure to the research community. In this paper, we perform
an in-depth investigation and observe that the efficacy of pseudo-LiDAR
representation comes from the coordinate transformation, instead of data
representation itself. Based on this observation, we design an image based CNN
detector named Patch-Net, which is more generalized and can be instantiated as
pseudo-LiDAR based 3D detectors. Moreover, the pseudo-LiDAR data in our
PatchNet is organized as the image representation, which means existing 2D CNN
designs can be easily utilized for extracting deep features from input data and
boosting 3D detection performance. We conduct extensive experiments on the
challenging KITTI dataset, where the proposed PatchNet outperforms all existing
pseudo-LiDAR based counterparts. Code has been made available at:
this https URL

通过深入研究，观察到伪激光雷达表示法的功效来自于坐标变换，作者设计了一种名为 Patch-Net 的基于图像的 CNN 探测器，其具有更广泛的适用性并可作为伪激光雷达的 3D 探测器。PatchNet 的伪激光雷达数据是以图像表示的，可以轻松地利用现有的 2D CNN 设计从输入数据中提取深度特征，提高 3D 检测性能。在具有挑战性的 KITTI 数据集上进行了广泛实验，证明了所提出的 PatchNet 优于所有现有的基于伪激光雷达的竞争对手。

重新思考伪 LiDAR 表征

Rethinking Pseudo-LiDAR Representation

The combination of a CNN detector and a search framework forms the basis for
local object/pattern detection. To handle the waste of regional information and
the defective compromise between efficiency and accuracy, this paper proposes a
probabilistic model with a powerful search framework. By mapping an image into
a probabilistic distribution of objects, this new model gives more informative
outputs with less computation. The setting and analytic traits are elaborated
in this paper, followed by a series of experiments carried out on FDDB, which
show that the proposed model is sound, efficient and analytic.

本文提出了一种基于概率模型和强大搜索框架的卷积神经网络探测器，旨在解决区域信息浪费和效率与准确性之间的权衡问题，通过将图像映射为对象的概率分布，该模型以更少的计算量提供更丰富的输出，并在 FDDB 上进行了一系列实验证明了该模型的可靠性、效率和解析性。