涉及的文章：

• 相近工作
  • [[Part-level Scene Reconstruction Affords Robot Interaction]]
  • [[Scene Reconstruction with Functional Objects for Robot Autonomy]]
  • [[Reasoning with Scene Graphs for Robot Planning under Partial Observability]]
  • [[ACDC- Automated Creation of Digital Cousins for Robust Policy Learning]]
  • [[CLIP-Fields- Weakly Supervised Semantic Fields for Robotic Memory]]
  • [[Factorizable Net= An Efficient Subgraph-based Framework for Scene Graph Generation]]
  • [[SayPlan= Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning]]
  • [[ConceptGraphs= Open-Vocabulary 3D Scene Graphs for Perception and Planning]]
• 数据生成
  • [[PHYSCENE- Physically Interactable 3D Scene Synthesis for Embodied AI]]
• 数据集
  • CLEVR
  • Visual Genome
• 其他
  • [[SceneGraphFusion- Incremental 3D Scene Graph Prediction from RGB-D Sequences]]
  • [[Visual Relationship Detection with Language Priors]]
  • [[Image generation from scene graphs]]
  • [[(FCSGG) Fully Convolutional Scene Graph Generation]]
  • [[RelTR= Relation Transformer for Scene Graph Generation]]
  • [[Scene Graph Generation by Iterative Message Passing]]
  • [[From Pixels to Graphs= Open-Vocabulary Scene Graph Generation with Vision-Language Models]]
  • [[(VtransE) Visual Translation Embedding Network for Visual Relation Detection]]
  • [[(UVtransE) Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation]]
  • [[(RLSV) Representation Learning for Scene Graph Completion via Jointly Structural and Visual Embedding]]
  • [[Energy-Based Learning for Scene Graph Generation]]

研究目标

通过构建part-level scene-graph，结合Reasoning with LLM 让机器人能够实现更复杂的交互，并以此完成更复杂的任务。

Scene Graph Introduction

Background

Visual scene understanding长期以来一直被认为是计算机视觉的圣杯

Rapid scene understanding at all levels

Generally

Visual scene understanding 可以被分为两块任务

• recognition task

  • image level
    • image classification
      • [[DINO]]
      • [[CLIP多模态预训练模型]]
  • pixel level
    • semantic segmentation: classify each pixel in an image into a category
      • Mask RCNN
      • U-Net
  • instance level
    • instance segmentation: detect and delineate each individual object instance in an image (bounding boxes or segmentation masks)
      • [[Grounding-DINO]]
      • [[Gounded-SAM]]
  • pixel & instance level
    • [[Panoptic Segmentation]]: takes into account both per-pixel class and instance labels
      • [[MaskDINO]]
      • [[Semantic-SAM]]
  

• application task

  • …

Relation & Interaction

但是以上这些Generally的工作注重的都是the localization of objects，更高级别的任务强调探索对象之间的丰富语义关系，以及对象与周围环境的相互作用

• 视觉关系检测（VRD）
  • [[GPS-Net= Graph Property Sensing Network for Scene Graph Generation]]
  • [[Large-scale visual relationship understanding]]
• 人类对象相互作用（HOI）
  • …

CV & NLP

除此之外还有将NLP和CV结合起来的方向，主要是一些VLM

• image caption
• visual question answering
• visual dialog

Structured Representation of Scene (Scene Graph)

对于总体场景的感知和信息的有效表示仍然是瓶颈。
所以Li Feifei 在[[Image Retrieval using Scene Graphs]]提出Scene Graph

与Structured Representation相对的是Latent Representation

Scene Graph Definition

A scene graph is a structural representation, which can capture detailed semantics by explicitly Modeling

• objects (‘‘man’’, ‘‘fire hydrant’’, ‘‘shorts’’)
• attributes of objects (‘‘fire hydrant is yellow’’)
• relations between paired objects (‘‘man jumping over fire hydrant’’)

A scene graph is a set of visual relationship triplets in the form of <subject, relation, object> or <object, is, attribute>

Scene graphs should serve as an objective semantic representation of the state of the scene

为什么选择scene graph

Scene Graph具有应对和改善其他视觉任务的内在潜力。
可以解决的视觉任务包括：

• Image captioning
  • take an image as an input and parse it into a scene graph, and then generate a reasonable text as output.
• Visual question answering
• Content-based image retrieval
• Image generation
  • extracting scene graphs from the text description and then generate realistic images
    • [[Image generation from scene graphs]]
• referring expression comprehension

Scene Graph Generation

场景图生成的目的是解析图像或一系列图像，并且生成结构化表示，以此弥合视觉和语义感知之间的差距，并最终达到对视觉场景的完整理解。
任务的本质是检测视觉关系。

先驱工作

早先由Feifei [[Visual Relationship Detection with Language Priors]] 提出了视觉关系检测的方法。
以及Visual Genome这个包含物体关系的数据集

生成方法

Two-stage

Detects objects first and then solves a classification task to determine the relationship between each pair of objects

**General:** a) 通过图片获取 subject/object and union box proposals (ROI感兴趣区域)

b) 提取每个区域的特征。包括object的appearance, spatial information, label, depth, and mask；predicate的appearance, spatial, depth, and mask。

• Fast/Faster R-CNN

c) 这些多模态特征被 vectorized, combined, and refined。可以通过：

• message passing mechanisms
  • [[Scene Graph Generation by Iterative Message Passing]]
• attention mechanisms
• visual translation embedding

d) 分类器用于预测predicate的类别

基于Visual translation embedding的

• Translation between Subject and Object (subject+predicate ≈ object)
  • [[(VtransE) Visual Translation Embedding Network for Visual Relation Detection]]
• Translation among Subject, Object and Predicate
  • [[(UVtransE) Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation]]
  • [[(RLSV) Representation Learning for Scene Graph Completion via Jointly Structural and Visual Embedding]]

One-stage!!!

Simultaneously detects and recognizes objects and relations
相较于two-stage:

• 需要更少的计算资源和参数
• 不会受到object detection的质量影响
  Example:
• [[(FCSGG) Fully Convolutional Scene Graph Generation]] (bottom-up + RAF)
• [[RelTR= Relation Transformer for Scene Graph Generation]] (bottom-up)
• [[SGTR= End-to-end Scene Graph Generation with Transformer]] (top-down)

Open-Vocabulary

基本都是基于LLM或者VLM之类的大模型

• [[From Pixels to Graphs= Open-Vocabulary Scene Graph Generation with Vision-Language Models]]
• [[ConceptGraphs= Open-Vocabulary 3D Scene Graphs for Perception and Planning]]

Scene Graph小结

这里所有的工作都是关于如何判断两个独立物体之间的谓语关系（例如riding, holding…），并没有涉及part-level relationship的工作。part-level的父子关系和object-level的谓语关系是很不一样的。

不基于Scene Graph 的场景理解方法

隐式场景

即场景信息存储在一个神经网络中，并没有显式的结构，规划器（可以是LLM）通过query这个模型来获得信息。

• [[CLIP-Fields- Weakly Supervised Semantic Fields for Robotic Memory]]
  • 无结构化，只提供语义查询，定位

数字表亲场景

核心思想是用交互更丰富的模型组合成可交互的替代场景。

• [[Scene Reconstruction with Functional Objects for Robot Autonomy]]
• [[ACDC- Automated Creation of Digital Cousins for Robust Policy Learning]]
  • 通既有的精细模型库来拟合场景中的物体，可以实现更丰富的交互，对家常物品zero-shot，但是精度有限，不能应对复杂物体

Contact Graph (可以认为是Scene Graph的扩展)

主要用于建模物体之间的运动学关系

• [[Scene Reconstruction with Functional Objects for Robot Autonomy]]
• [[Part-level Scene Reconstruction Affords Robot Interaction]]
  • 这个涉及到了父子之间的运动学关系

Scene Graph & Robots

将 scene graph 用于机器人任务理解和规划

• [[SayPlan= Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning]]
• [[Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation]]
• [[ConceptGraphs= Open-Vocabulary 3D Scene Graphs for Perception and Planning]]

和李飞飞[[ACDC- Automated Creation of Digital Cousins for Robust Policy Learning]]的思想类似。

参考： https://www.youtube.com/watch?v=T35ba_VXkMY&t=1744s

DETR是一个使用transformer作为基本架构的 object detection 模型。

Insight

Object queries (something that can be learned):

>Visualization of all box predictions on all images from COCO 2017 val set for 20 out of total N = 100 prediction slots in DETR decoder. Each box prediction is represented as a point with the coordinates of its center in the 1-by-1 square normalized by each image size. The points are color-coded so that green color corresponds to small boxes, red to large horizontal boxes and blue to large vertical boxes. We observe that each slot learns to specialize on certain areas and box sizes with several operating modes. We note that almost all slots have a mode of predicting large image-wide boxes that are common in COCO dataset.

这片文章可以成为场景物理重建的基石之一
类似的后续工作有OMG-Seg

是什么

通用图像分割模型，以实现细分并识别任何所需粒度的任何内容

• Semantic-awareness
• Granularity-abundance

数据集

### 难点 - 目前的一些通用的物体和分割数据集虽然确实提供了大体量的数据和丰富的语义信息，但只局限在object level。 - 目前一些分割了细分part的数据集却体量有限。 - SAM使用的数据集为多粒度的大体量数据集，但是并不包含语义标注。 ### 解决方式 合并了多个不同分割粒度的数据集

注：此DINO并非自蒸馏自监督的那个[[DINO]]，而是派生自

## Align Before Fuse ### Image Encoder 标准的六层self attention ViT，初始化为DeiT论文中的在ImageNet-1K数据集上训练的参数

Text Encoder

使用的backbone是BERT(通过MLM训练)
该研究认为，image encoder的模型大小应该大于text encoder,所以在text encoder这里，只使用六层self attention来提取特征，剩余六层cross attention用于multi-modal encoder。

ITC Loss & Momentum

参考Moco [[Moco- Momentum Contrast for Unsupervised Visual Representation Learning]]

Improve Noisy Web Data

见[[BLIP]]，是沿用的工作

Loss

#### ITC 旨在在融合之前学习更好的单模态表示

ITM

MLM