Scene Graph Generation (SGG) is a challenging task of detecting objects and
predicting relationships between objects. After DETR was developed, one-stage
SGG models based on a one-stage object detector have been actively studied.
However, complex modeling is used to predict the relationship between objects,
and the inherent relationship between object queries learned in the multi-head
self-attention of the object detector has been neglected. We propose a
lightweight one-stage SGG model that extracts the relation graph from the
various relationships learned in the multi-head self-attention layers of the
DETR decoder. By fully utilizing the self-attention by-products, the relation
graph can be extracted effectively with a shallow relation extraction head.
Considering the dependency of the relation extraction task on the object
detection task, we propose a novel relation smoothing technique that adjusts
the relation label adaptively according to the quality of the detected objects.
By the relation smoothing, the model is trained according to the continuous
curriculum that focuses on object detection task at the beginning of training
and performs multi-task learning as the object detection performance gradually
improves. Furthermore, we propose a connectivity prediction task that predicts
whether a relation exists between object pairs as an auxiliary task of the
relation extraction. We demonstrate the effectiveness and efficiency of our
method for the Visual Genome and Open Image V6 datasets. Our code is publicly
available at this https URL .

提出一种轻量级的一阶场景图生成（SGG）模型，通过充分利用 DETR 解码器的多头自注意力层中学习到的各种关系，使用浅层关系抽取头有效地提取关系图，并考虑到关系抽取任务对于物体检测任务的依赖，提出了一种新颖的关系平滑技术，根据检测到的物体质量自适应地调整关系标签，同时还提出了一个辅助任务 —— 连接性预测任务来预测物体对之间是否存在关系。

从 Transformer 中提取出图形用于场景图生成的 EGTR

EGTR: Extracting Graph from Transformer for Scene Graph Generation

We are interested in reconstructing the mesh representation of object
surfaces from point clouds. Surface reconstruction is a prerequisite for
downstream applications such as rendering, collision avoidance for planning,
animation, etc. However, the task is challenging if the input point cloud has a
low resolution, which is common in real-world scenarios (e.g., from LiDAR or
Kinect sensors). Existing learning-based mesh generative methods mostly predict
the surface by first building a shape embedding that is at the whole object
level, a design that causes issues in generating fine-grained details and
generalizing to unseen categories. Instead, we propose to leverage the input
point cloud as much as possible, by only adding connectivity information to
existing points. Particularly, we predict which triplets of points should form
faces. Our key innovation is a surrogate of local connectivity, calculated by
comparing the intrinsic/extrinsic metrics. We learn to predict this surrogate
using a deep point cloud network and then feed it to an efficient
post-processing module for high-quality mesh generation. We demonstrate that
our method can not only preserve details, handle ambiguous structures, but also
possess strong generalizability to unseen categories by experiments on
synthetic and real data. The code is available at
this https URL

本文提出了一种新的基于深度学习的方法，旨在从点云中重建物体表面网格表示，通过预测点云中三元组之间的连通性关系来生成高质量网格，并证明该方法在处理细节保留、模糊结构等实际问题时表现良好。